{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:06:41Z","timestamp":1775326001124,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":68,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,7]],"date-time":"2022-08-07T00:00:00Z","timestamp":1659830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,8,7]]},"DOI":"10.1145\/3528233.3530738","type":"proceedings-article","created":{"date-parts":[[2022,7,20]],"date-time":"2022-07-20T13:56:43Z","timestamp":1658325403000},"page":"1-10","source":"Crossref","is-referenced-by-count":267,"title":["StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets"],"prefix":"10.1145","author":[{"given":"Axel","family":"Sauer","sequence":"first","affiliation":[{"name":"University of T\u00fcbingen, Germany and Max Planck Institute for Intelligent Systems, Germany"}]},{"given":"Katja","family":"Schwarz","sequence":"additional","affiliation":[{"name":"University of T\u00fcbingen, Germany and Max Planck Institute for Intelligent Systems, Germany"}]},{"given":"Andreas","family":"Geiger","sequence":"additional","affiliation":[{"name":"University of T\u00fcbingen, Germany and Max Planck Institute for Intelligent Systems, Germany"}]}],"member":"320","published-online":{"date-parts":[[2022,8,7]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Proc. of the IEEE International Conf. on Computer Vision (ICCV). 4431\u20134440","author":"Abdal Rameen","year":"2019","unstructured":"Rameen Abdal , Yipeng Qin , and Peter Wonka . 2019 . Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space? . In Proc. of the IEEE International Conf. on Computer Vision (ICCV). 4431\u20134440 . https:\/\/doi.org\/10.1109\/ICCV.2019.00453 Rameen Abdal, Yipeng Qin, and Peter Wonka. 2019. Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?. In Proc. of the IEEE International Conf. on Computer Vision (ICCV). 4431\u20134440. https:\/\/doi.org\/10.1109\/ICCV.2019.00453"},{"key":"e_1_3_2_1_2_1","article-title":"StyleFlow: Attribute-conditioned Exploration of StyleGAN-Generated Images using Conditional Continuous Normalizing Flows","volume":"40","author":"Abdal Rameen","year":"2021","unstructured":"Rameen Abdal , Peihao Zhu , Niloy\u00a0 J. Mitra , and Peter Wonka . 2021 . StyleFlow: Attribute-conditioned Exploration of StyleGAN-Generated Images using Conditional Continuous Normalizing Flows . ACM Trans. on Graphics 40 , 3 (2021), 21:1\u201321:21. https:\/\/doi.org\/10.1145\/3447648 Rameen Abdal, Peihao Zhu, Niloy\u00a0J. Mitra, and Peter Wonka. 2021. StyleFlow: Attribute-conditioned Exploration of StyleGAN-Generated Images using Conditional Continuous Normalizing Flows. ACM Trans. on Graphics 40, 3 (2021), 21:1\u201321:21. https:\/\/doi.org\/10.1145\/3447648","journal-title":"ACM Trans. on Graphics"},{"key":"e_1_3_2_1_3_1","volume-title":"Proc. of the IEEE International Conf. on Computer Vision (ICCV) abs\/2104","author":"Alaluf Yuval","year":"2021","unstructured":"Yuval Alaluf , Or Patashnik , and Daniel Cohen-Or . 2021 . ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement . Proc. of the IEEE International Conf. on Computer Vision (ICCV) abs\/2104 .02699(2021). https:\/\/arxiv.org\/abs\/2104.02699 Yuval Alaluf, Or Patashnik, and Daniel Cohen-Or. 2021. ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement. Proc. of the IEEE International Conf. on Computer Vision (ICCV) abs\/2104.02699(2021). https:\/\/arxiv.org\/abs\/2104.02699"},{"key":"e_1_3_2_1_4_1","volume-title":"Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=B1xsqj09Fm","author":"Brock Andrew","year":"2019","unstructured":"Andrew Brock , Jeff Donahue , and Karen Simonyan . 2019 . Large Scale GAN Training for High Fidelity Natural Image Synthesis . In Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=B1xsqj09Fm Andrew Brock, Jeff Donahue, and Karen Simonyan. 2019. Large Scale GAN Training for High Fidelity Natural Image Synthesis. In Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=B1xsqj09Fm"},{"key":"e_1_3_2_1_5_1","unstructured":"Arantxa Casanova Marl\u00e8ne Careil Jakob Verbeek Michal Drozdzal and Adriana Romero-Soriano. 2021. Instance-Conditioned GAN. In Advances in Neural Information Processing Systems (NeurIPS).  Arantxa Casanova Marl\u00e8ne Careil Jakob Verbeek Michal Drozdzal and Adriana Romero-Soriano. 2021. Instance-Conditioned GAN. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_1_6_1","volume-title":"Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). Computer Vision Foundation \/ IEEE, 14997\u201315007","author":"Chai Lucy","year":"2021","unstructured":"Lucy Chai , Jun-Yan Zhu , Eli Shechtman , Phillip Isola , and Richard Zhang . 2021 . Ensembling With Deep Generative Views . In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). Computer Vision Foundation \/ IEEE, 14997\u201315007 . https:\/\/openaccess.thecvf.com\/content\/CVPR2021\/html\/Chai_Ensembling_With_Deep_Generative_Views_CVPR_2021_paper.html Lucy Chai, Jun-Yan Zhu, Eli Shechtman, Phillip Isola, and Richard Zhang. 2021. Ensembling With Deep Generative Views. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). Computer Vision Foundation \/ IEEE, 14997\u201315007. https:\/\/openaccess.thecvf.com\/content\/CVPR2021\/html\/Chai_Ensembling_With_Deep_Generative_Views_CVPR_2021_paper.html"},{"key":"e_1_3_2_1_7_1","unstructured":"Xinlei Chen Haoqi Fan and Ross Girshick\u00a0Kaiming He. 2020. Improved baselines with momentum contrastive learning. arxiv.org:2003.04297  Xinlei Chen Haoqi Fan and Ross Girshick\u00a0Kaiming He. 2020. Improved baselines with momentum contrastive learning. arxiv.org:2003.04297"},{"key":"e_1_3_2_1_8_1","volume-title":"Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). Computer Vision Foundation \/ IEEE, 5770\u20135779","author":"Collins Edo","year":"2020","unstructured":"Edo Collins , Raja Bala , Bob Price , and Sabine S\u00fcsstrunk . 2020 . Editing in Style: Uncovering the Local Semantics of GANs . In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). Computer Vision Foundation \/ IEEE, 5770\u20135779 . https:\/\/doi.org\/10.1109\/CVPR42600.2020.00581 Edo Collins, Raja Bala, Bob Price, and Sabine S\u00fcsstrunk. 2020. Editing in Style: Uncovering the Local Semantics of GANs. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). Computer Vision Foundation \/ IEEE, 5770\u20135779. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00581"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2875194"},{"key":"e_1_3_2_1_10_1","volume-title":"Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, 248\u2013255","author":"Deng Jia","year":"2009","unstructured":"Jia Deng , Wei Dong , Richard Socher , Li-Jia Li , Kai Li , and Li Fei-Fei . 2009 . ImageNet: A large-scale hierarchical image database . In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, 248\u2013255 . https:\/\/doi.org\/10.1109\/CVPR.2009.5206848 Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. ImageNet: A large-scale hierarchical image database. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, 248\u2013255. https:\/\/doi.org\/10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_1_11_1","unstructured":"Prafulla Dhariwal and Alex Nichol. 2021. Diffusion Models Beat GANs on Image Synthesis. (2021).  Prafulla Dhariwal and Alex Nichol. 2021. Diffusion Models Beat GANs on Image Synthesis. (2021)."},{"key":"e_1_3_2_1_12_1","volume-title":"Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=YicbFdNTTy","author":"Dosovitskiy Alexey","year":"2021","unstructured":"Alexey Dosovitskiy , Lucas Beyer , Alexander Kolesnikov , Dirk Weissenborn , Xiaohua Zhai , Thomas Unterthiner , Mostafa Dehghani , Matthias Minderer , Georg Heigold , Sylvain Gelly , Jakob Uszkoreit , and Neil Houlsby . 2021 . An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale . In Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=YicbFdNTTy Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=YicbFdNTTy"},{"key":"e_1_3_2_1_13_1","volume-title":"Proc. IEEE International Conf. on Robotics and Automation (ICRA). IEEE, 3376\u20133383","author":"Fregin Andreas","year":"2018","unstructured":"Andreas Fregin , Julian M\u00fcller , Ulrich Krebel , and Klaus Dietmayer . 2018 . The DriveU Traffic Light Dataset: Introduction and Comparison with Existing Datasets . In Proc. IEEE International Conf. on Robotics and Automation (ICRA). IEEE, 3376\u20133383 . https:\/\/doi.org\/10.1109\/ICRA.2018.8460737 Andreas Fregin, Julian M\u00fcller, Ulrich Krebel, and Klaus Dietmayer. 2018. The DriveU Traffic Light Dataset: Introduction and Comparison with Existing Datasets. In Proc. IEEE International Conf. on Robotics and Automation (ICRA). IEEE, 3376\u20133383. https:\/\/doi.org\/10.1109\/ICRA.2018.8460737"},{"key":"e_1_3_2_1_14_1","volume-title":"Proc. of the IEEE International Conf. on Computer Vision (ICCV). IEEE, 5743\u20135752","author":"Goetschalckx Lore","year":"2019","unstructured":"Lore Goetschalckx , Alex Andonian , Aude Oliva , and Phillip Isola . 2019 . GANalyze: Toward Visual Definitions of Cognitive Image Properties . In Proc. of the IEEE International Conf. on Computer Vision (ICCV). IEEE, 5743\u20135752 . https:\/\/doi.org\/10.1109\/ICCV.2019.00584 Lore Goetschalckx, Alex Andonian, Aude Oliva, and Phillip Isola. 2019. GANalyze: Toward Visual Definitions of Cognitive Image Properties. In Proc. of the IEEE International Conf. on Computer Vision (ICCV). IEEE, 5743\u20135752. https:\/\/doi.org\/10.1109\/ICCV.2019.00584"},{"key":"e_1_3_2_1_15_1","volume-title":"Advances in Neural Information Processing Systems (NeurIPS), Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil\u00a0D","author":"Goodfellow J.","year":"2014","unstructured":"Ian\u00a0 J. Goodfellow , Jean Pouget-Abadie , Mehdi Mirza , Bing Xu , David Warde-Farley , Sherjil Ozair , Aaron\u00a0 C. Courville , and Yoshua Bengio . 2014. Generative Adversarial Nets . In Advances in Neural Information Processing Systems (NeurIPS), Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil\u00a0D . Lawrence, and Kilian\u00a0Q. Weinberger (Eds.). 2672\u20132680. https:\/\/proceedings.neurips.cc\/paper\/ 2014 \/hash\/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html Ian\u00a0J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron\u00a0C. Courville, and Yoshua Bengio. 2014. Generative Adversarial Nets. In Advances in Neural Information Processing Systems (NeurIPS), Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil\u00a0D. Lawrence, and Kilian\u00a0Q. Weinberger (Eds.). 2672\u20132680. https:\/\/proceedings.neurips.cc\/paper\/2014\/hash\/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html"},{"key":"e_1_3_2_1_16_1","unstructured":"Tom\u00a0George Grigg Dan Busbridge Jason Ramapuram and Russ Webb. 2021. Do Self-Supervised and Supervised Methods Learn Similar Visual Representations?arxiv.org:2110.00528  Tom\u00a0George Grigg Dan Busbridge Jason Ramapuram and Russ Webb. 2021. Do Self-Supervised and Supervised Methods Learn Similar Visual Representations?arxiv.org:2110.00528"},{"key":"e_1_3_2_1_17_1","volume-title":"Proc. of the International Conf. on Learning Representations (ICLR). https:\/\/openreview.net\/forum?id=4Ycr8oeCoIh","author":"Grigoryev Timofey","year":"2022","unstructured":"Timofey Grigoryev , Andrey Voynov , and Artem Babenko . 2022 . When, Why, and Which Pretrained GANs Are Useful? . In Proc. of the International Conf. on Learning Representations (ICLR). https:\/\/openreview.net\/forum?id=4Ycr8oeCoIh Timofey Grigoryev, Andrey Voynov, and Artem Babenko. 2022. When, Why, and Which Pretrained GANs Are Useful?. In Proc. of the International Conf. on Learning Representations (ICLR). https:\/\/openreview.net\/forum?id=4Ycr8oeCoIh"},{"key":"e_1_3_2_1_18_1","volume-title":"Retrieved","year":"2020","unstructured":"Gwern. 2020 . Making Anime Faces with StyleGAN . Retrieved January 16, 2022 from https:\/\/www.gwern.net\/Faces#stylegan2-ext-modifications\/ Gwern. 2020. Making Anime Faces with StyleGAN. Retrieved January 16, 2022 from https:\/\/www.gwern.net\/Faces#stylegan2-ext-modifications\/"},{"key":"e_1_3_2_1_19_1","unstructured":"Erik H\u00e4rk\u00f6nen Aaron Hertzmann Jaakko Lehtinen and Sylvain Paris. 2020. GANSpace: Discovering Interpretable GAN Controls. In Advances in Neural Information Processing Systems (NeurIPS). https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/6fe43269967adbb64ec6149852b5cc3e-Abstract.html  Erik H\u00e4rk\u00f6nen Aaron Hertzmann Jaakko Lehtinen and Sylvain Paris. 2020. GANSpace: Discovering Interpretable GAN Controls. In Advances in Neural Information Processing Systems (NeurIPS). https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/6fe43269967adbb64ec6149852b5cc3e-Abstract.html"},{"key":"e_1_3_2_1_20_1","unstructured":"Martin Heusel Hubert Ramsauer Thomas Unterthiner Bernhard Nessler and Sepp Hochreiter. 2017. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. In Advances in Neural Information Processing Systems (NeurIPS). 6626\u20136637. https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/8a1d694707eb0fefe65871369074926d-Abstract.html  Martin Heusel Hubert Ramsauer Thomas Unterthiner Bernhard Nessler and Sepp Hochreiter. 2017. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. In Advances in Neural Information Processing Systems (NeurIPS). 6626\u20136637. https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/8a1d694707eb0fefe65871369074926d-Abstract.html"},{"key":"e_1_3_2_1_21_1","article-title":"Cascaded Diffusion Models for High Fidelity Image Generation","volume":"23","author":"Ho Jonathan","year":"2022","unstructured":"Jonathan Ho , Chitwan Saharia , William Chan , David\u00a0 J. Fleet , Mohammad Norouzi , and Tim Salimans . 2022 . Cascaded Diffusion Models for High Fidelity Image Generation . J. Mach. Learn. Res. 23 (2022), 47:1\u201347:33. Jonathan Ho, Chitwan Saharia, William Chan, David\u00a0J. Fleet, Mohammad Norouzi, and Tim Salimans. 2022. Cascaded Diffusion Models for High Fidelity Image Generation. J. Mach. Learn. Res. 23(2022), 47:1\u201347:33.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_1_22_1","volume-title":"Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=HylsTT4FvB","author":"Jahanian Ali","year":"2020","unstructured":"Ali Jahanian , Lucy Chai , and Phillip Isola . 2020 . On the \u201dsteerability\u201d of generative adversarial networks . In Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=HylsTT4FvB Ali Jahanian, Lucy Chai, and Phillip Isola. 2020. On the \u201dsteerability\u201d of generative adversarial networks. In Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=HylsTT4FvB"},{"key":"e_1_3_2_1_23_1","volume-title":"Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=Hk99zCeAb","author":"Karras Tero","year":"2018","unstructured":"Tero Karras , Timo Aila , Samuli Laine , and Jaakko Lehtinen . 2018 . Progressive Growing of GANs for Improved Quality, Stability, and Variation . In Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=Hk99zCeAb Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. 2018. Progressive Growing of GANs for Improved Quality, Stability, and Variation. In Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=Hk99zCeAb"},{"key":"e_1_3_2_1_24_1","unstructured":"Tero Karras Miika Aittala Janne Hellsten Samuli Laine Jaakko Lehtinen and Timo Aila. 2020a. Training Generative Adversarial Networks with Limited Data. In Advances in Neural Information Processing Systems (NeurIPS). https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/8d30aa96e72440759f74bd2306c1fa3d-Abstract.html  Tero Karras Miika Aittala Janne Hellsten Samuli Laine Jaakko Lehtinen and Timo Aila. 2020a. Training Generative Adversarial Networks with Limited Data. In Advances in Neural Information Processing Systems (NeurIPS). https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/8d30aa96e72440759f74bd2306c1fa3d-Abstract.html"},{"key":"e_1_3_2_1_25_1","unstructured":"Tero Karras Miika Aittala Samuli Laine Erik H\u00e4rk\u00f6nen Janne Hellsten Jaakko Lehtinen and Timo Aila. 2021. Alias-Free Generative Adversarial Networks. In Advances in Neural Information Processing Systems (NeurIPS).  Tero Karras Miika Aittala Samuli Laine Erik H\u00e4rk\u00f6nen Janne Hellsten Jaakko Lehtinen and Timo Aila. 2021. Alias-Free Generative Adversarial Networks. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_1_26_1","volume-title":"Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). Computer Vision Foundation \/ IEEE, 4401\u20134410","author":"Karras Tero","year":"2019","unstructured":"Tero Karras , Samuli Laine , and Timo Aila . 2019 . A Style-Based Generator Architecture for Generative Adversarial Networks . In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). Computer Vision Foundation \/ IEEE, 4401\u20134410 . https:\/\/doi.org\/10.1109\/CVPR.2019.00453 Tero Karras, Samuli Laine, and Timo Aila. 2019. A Style-Based Generator Architecture for Generative Adversarial Networks. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). Computer Vision Foundation \/ IEEE, 4401\u20134410. https:\/\/doi.org\/10.1109\/CVPR.2019.00453"},{"key":"e_1_3_2_1_27_1","volume-title":"Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). Computer Vision Foundation \/ IEEE, 8107\u20138116","author":"Karras Tero","year":"2020","unstructured":"Tero Karras , Samuli Laine , Miika Aittala , Janne Hellsten , Jaakko Lehtinen , and Timo Aila . 2020 b. Analyzing and Improving the Image Quality of StyleGAN . In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). Computer Vision Foundation \/ IEEE, 8107\u20138116 . https:\/\/doi.org\/10.1109\/CVPR42600.2020.00813 Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. 2020b. Analyzing and Improving the Image Quality of StyleGAN. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). Computer Vision Foundation \/ IEEE, 8107\u20138116. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00813"},{"key":"e_1_3_2_1_28_1","volume-title":"Proc. of the IEEE Winter Conference on Applications of Computer Vision (WACV)","author":"Kocasari Umut","year":"2022","unstructured":"Umut Kocasari , Alara Dirik , Mert Tiftikci , and Pinar Yanardag . 2022 . StyleMC: Multi-Channel Based Fast Text-Guided Image Generation and Manipulation . Proc. of the IEEE Winter Conference on Applications of Computer Vision (WACV) (2022). https:\/\/arxiv.org\/abs\/2112.08493 Umut Kocasari, Alara Dirik, Mert Tiftikci, and Pinar Yanardag. 2022. StyleMC: Multi-Channel Based Fast Text-Guided Image Generation and Manipulation. Proc. of the IEEE Winter Conference on Applications of Computer Vision (WACV) (2022). https:\/\/arxiv.org\/abs\/2112.08493"},{"key":"e_1_3_2_1_29_1","unstructured":"Alex Krizhevsky Geoffrey Hinton 2009. Learning multiple layers of features from tiny images. (2009).  Alex Krizhevsky Geoffrey Hinton 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_1_30_1","unstructured":"Tuomas Kynk\u00e4\u00e4nniemi Tero Karras Samuli Laine Jaakko Lehtinen and Timo Aila. 2019. Improved Precision and Recall Metric for Assessing Generative Models. In Advances in Neural Information Processing Systems (NeurIPS). https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/0234c510bc6d908b28c70ff313743079-Abstract.html  Tuomas Kynk\u00e4\u00e4nniemi Tero Karras Samuli Laine Jaakko Lehtinen and Timo Aila. 2019. Improved Precision and Recall Metric for Assessing Generative Models. In Advances in Neural Information Processing Systems (NeurIPS). https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/0234c510bc6d908b28c70ff313743079-Abstract.html"},{"key":"e_1_3_2_1_31_1","volume-title":"BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotations. arXiv.org","author":"Li Daiqing","year":"2022","unstructured":"Daiqing Li , Huan Ling , Seung\u00a0Wook Kim , Karsten Kreis , Adela Barriuso , Sanja Fidler , and Antonio Torralba . 2022. BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotations. arXiv.org ( 2022 ). https:\/\/arxiv.org\/abs\/2201.04684 Daiqing Li, Huan Ling, Seung\u00a0Wook Kim, Karsten Kreis, Adela Barriuso, Sanja Fidler, and Antonio Torralba. 2022. BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotations. arXiv.org (2022). https:\/\/arxiv.org\/abs\/2201.04684"},{"key":"e_1_3_2_1_32_1","volume-title":"Proc. of the IEEE International Conf. on Computer Vision (ICCV) Workshops. IEEE","author":"Liang Jingyun","year":"2021","unstructured":"Jingyun Liang , Jiezhang Cao , Guolei Sun , Kai Zhang , Luc\u00a0Van Gool , and Radu Timofte . 2021 . SwinIR: Image Restoration Using Swin Transformer . In Proc. of the IEEE International Conf. on Computer Vision (ICCV) Workshops. IEEE , 1833\u20131844. https:\/\/doi.org\/10.1109\/ICCVW54120.2021.00210 Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc\u00a0Van Gool, and Radu Timofte. 2021. SwinIR: Image Restoration Using Swin Transformer. In Proc. of the IEEE International Conf. on Computer Vision (ICCV) Workshops. IEEE, 1833\u20131844. https:\/\/doi.org\/10.1109\/ICCVW54120.2021.00210"},{"key":"e_1_3_2_1_33_1","unstructured":"Huan Ling Karsten Kreis Daiqing Li Seung\u00a0Wook Kim Antonio Torralba and Sanja Fidler. 2021. EditGAN: High-Precision Semantic Image Editing. arXiv.org abs\/2111.03186(2021). https:\/\/arxiv.org\/abs\/2111.03186  Huan Ling Karsten Kreis Daiqing Li Seung\u00a0Wook Kim Antonio Torralba and Sanja Fidler. 2021. EditGAN: High-Precision Semantic Image Editing. arXiv.org abs\/2111.03186(2021). https:\/\/arxiv.org\/abs\/2111.03186"},{"key":"e_1_3_2_1_34_1","volume-title":"Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=1Fqg133qRaI","author":"Liu Bingchen","year":"2021","unstructured":"Bingchen Liu , Yizhe Zhu , Kunpeng Song , and Ahmed Elgammal . 2021 . Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis . In Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=1Fqg133qRaI Bingchen Liu, Yizhe Zhu, Kunpeng Song, and Ahmed Elgammal. 2021. Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis. In Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=1Fqg133qRaI"},{"key":"e_1_3_2_1_35_1","volume-title":"Proc. of the International Conf. on Machine learning (ICML)(Proceedings of Machine Learning Research, Vol.\u00a080)","author":"Mescheder M.","year":"2018","unstructured":"Lars\u00a0 M. Mescheder , Andreas Geiger , and Sebastian Nowozin . 2018 . Which Training Methods for GANs do actually Converge? . In Proc. of the International Conf. on Machine learning (ICML)(Proceedings of Machine Learning Research, Vol.\u00a080) . PMLR, 3478\u20133487. http:\/\/proceedings.mlr.press\/v80\/mescheder18a.html Lars\u00a0M. Mescheder, Andreas Geiger, and Sebastian Nowozin. 2018. Which Training Methods for GANs do actually Converge?. In Proc. of the International Conf. on Machine learning (ICML)(Proceedings of Machine Learning Research, Vol.\u00a080). PMLR, 3478\u20133487. http:\/\/proceedings.mlr.press\/v80\/mescheder18a.html"},{"key":"e_1_3_2_1_36_1","volume-title":"Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=B1QRgziT-","author":"Miyato Takeru","year":"2018","unstructured":"Takeru Miyato , Toshiki Kataoka , Masanori Koyama , and Yuichi Yoshida . 2018 . Spectral Normalization for Generative Adversarial Networks . In Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=B1QRgziT- Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida. 2018. Spectral Normalization for Generative Adversarial Networks. In Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=B1QRgziT-"},{"key":"e_1_3_2_1_37_1","volume-title":"Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=ByS1VpgRZ","author":"Miyato Takeru","year":"2018","unstructured":"Takeru Miyato and Masanori Koyama . 2018 . cGANs with Projection Discriminator . In Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=ByS1VpgRZ Takeru Miyato and Masanori Koyama. 2018. cGANs with Projection Discriminator. In Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=ByS1VpgRZ"},{"key":"e_1_3_2_1_38_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning, ICML 2020","author":"Naeem Muhammad\u00a0Ferjad","year":"2020","unstructured":"Muhammad\u00a0Ferjad Naeem , Seong\u00a0Joon Oh , Youngjung Uh , Yunjey Choi , and Jaejun Yoo . 2020 . Reliable Fidelity and Diversity Metrics for Generative Models . In Proceedings of the 37th International Conference on Machine Learning, ICML 2020 , 13-18 July 2020, Virtual Event(Proceedings of Machine Learning Research, Vol.\u00a0119). 7176\u20137185. http:\/\/proceedings.mlr.press\/v119\/naeem20a.html Muhammad\u00a0Ferjad Naeem, Seong\u00a0Joon Oh, Youngjung Uh, Yunjey Choi, and Jaejun Yoo. 2020. Reliable Fidelity and Diversity Metrics for Generative Models. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event(Proceedings of Machine Learning Research, Vol.\u00a0119). 7176\u20137185. http:\/\/proceedings.mlr.press\/v119\/naeem20a.html"},{"key":"e_1_3_2_1_39_1","volume-title":"Proc. of the International Conf. on Machine learning (ICML). http:\/\/proceedings.mlr.press\/v139\/nash21a.html","author":"Nash Charlie","year":"2021","unstructured":"Charlie Nash , Jacob Menick , Sander Dieleman , and Peter\u00a0 W. Battaglia . 2021 . Generating images with sparse representations . In Proc. of the International Conf. on Machine learning (ICML). http:\/\/proceedings.mlr.press\/v139\/nash21a.html Charlie Nash, Jacob Menick, Sander Dieleman, and Peter\u00a0W. Battaglia. 2021. Generating images with sparse representations. In Proc. of the International Conf. on Machine learning (ICML). http:\/\/proceedings.mlr.press\/v139\/nash21a.html"},{"key":"e_1_3_2_1_40_1","volume-title":"Proc. of the International Conf. on Machine learning (ICML)(Proceedings of Machine Learning Research, Vol.\u00a0139)","author":"Nichol Alexander\u00a0Quinn","year":"2021","unstructured":"Alexander\u00a0Quinn Nichol and Prafulla Dhariwal . 2021 . Improved Denoising Diffusion Probabilistic Models . In Proc. of the International Conf. on Machine learning (ICML)(Proceedings of Machine Learning Research, Vol.\u00a0139) . PMLR, 8162\u20138171. http:\/\/proceedings.mlr.press\/v139\/nichol21a.html Alexander\u00a0Quinn Nichol and Prafulla Dhariwal. 2021. Improved Denoising Diffusion Probabilistic Models. In Proc. of the International Conf. on Machine learning (ICML)(Proceedings of Machine Learning Research, Vol.\u00a0139). PMLR, 8162\u20138171. http:\/\/proceedings.mlr.press\/v139\/nichol21a.html"},{"key":"e_1_3_2_1_41_1","volume-title":"Proc. of the IEEE International Conf. on Computer Vision (ICCV). 2085\u20132094","author":"Patashnik Or","year":"2021","unstructured":"Or Patashnik , Zongze Wu , Eli Shechtman , Daniel Cohen-Or , and Dani Lischinski . 2021 . StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery . In Proc. of the IEEE International Conf. on Computer Vision (ICCV). 2085\u20132094 . Or Patashnik, Zongze Wu, Eli Shechtman, Daniel Cohen-Or, and Dani Lischinski. 2021. StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery. In Proc. of the IEEE International Conf. on Computer Vision (ICCV). 2085\u20132094."},{"key":"e_1_3_2_1_42_1","volume-title":"Bogdan\u00a0C. Raducanu, and Jose\u00a0M. \u00c1lvarez.","author":"Perarnau Guim","year":"2016","unstructured":"Guim Perarnau , Joost van\u00a0de Weijer , Bogdan\u00a0C. Raducanu, and Jose\u00a0M. \u00c1lvarez. 2016 . Invertible Conditional GANs for image editing. arXiv.org abs\/1611.06355(2016). http:\/\/arxiv.org\/abs\/1611.06355 Guim Perarnau, Joost van\u00a0de Weijer, Bogdan\u00a0C. Raducanu, and Jose\u00a0M. \u00c1lvarez. 2016. Invertible Conditional GANs for image editing. arXiv.org abs\/1611.06355(2016). http:\/\/arxiv.org\/abs\/1611.06355"},{"key":"e_1_3_2_1_43_1","unstructured":"Etienne Perot Pierre de Tournemire Davide Nitti Jonathan Masci and Amos Sironi. 2020. Learning to Detect Objects with a 1 Megapixel Event Camera. In Advances in Neural Information Processing Systems (NeurIPS). https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/c213877427b46fa96cff6c39e837ccee-Abstract.html  Etienne Perot Pierre de Tournemire Davide Nitti Jonathan Masci and Amos Sironi. 2020. Learning to Detect Objects with a 1 Megapixel Event Camera. In Advances in Neural Information Processing Systems (NeurIPS). https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/c213877427b46fa96cff6c39e837ccee-Abstract.html"},{"key":"e_1_3_2_1_44_1","volume-title":"Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=XJk19XzGq2J","author":"Pope Phillip","year":"2021","unstructured":"Phillip Pope , Chen Zhu , Ahmed Abdelkader , Micah Goldblum , and Tom Goldstein . 2021 . The Intrinsic Dimension of Images and Its Impact on Learning . In Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=XJk19XzGq2J Phillip Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, and Tom Goldstein. 2021. The Intrinsic Dimension of Images and Its Impact on Learning. In Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=XJk19XzGq2J"},{"key":"e_1_3_2_1_45_1","volume-title":"Proc. of the International Conf. on Learning Representations (ICLR), Yoshua Bengioand Yann LeCun (Eds.). http:\/\/arxiv.org\/abs\/1511","author":"Radford Alec","year":"2016","unstructured":"Alec Radford , Luke Metz , and Soumith Chintala . 2016 . Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks . In Proc. of the International Conf. on Learning Representations (ICLR), Yoshua Bengioand Yann LeCun (Eds.). http:\/\/arxiv.org\/abs\/1511 .06434 Alec Radford, Luke Metz, and Soumith Chintala. 2016. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. In Proc. of the International Conf. on Learning Representations (ICLR), Yoshua Bengioand Yann LeCun (Eds.). http:\/\/arxiv.org\/abs\/1511.06434"},{"key":"e_1_3_2_1_46_1","unstructured":"Maithra Raghu Thomas Unterthiner Simon Kornblith Chiyuan Zhang and Alexey Dosovitskiy. 2021. Do Vision Transformers See Like Convolutional Neural Networks?. In Advances in Neural Information Processing Systems (NeurIPS).  Maithra Raghu Thomas Unterthiner Simon Kornblith Chiyuan Zhang and Alexey Dosovitskiy. 2021. Do Vision Transformers See Like Convolutional Neural Networks?. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_1_47_1","volume-title":"Pivotal Tuning for Latent-based Editing of Real Images. arXiv.org","author":"Roich Daniel","year":"2021","unstructured":"Daniel Roich , Ron Mokady , Amit\u00a0 H. Bermano , and Daniel Cohen-Or . 2021. Pivotal Tuning for Latent-based Editing of Real Images. arXiv.org ( 2021 ). https:\/\/arxiv.org\/abs\/2106.05744 Daniel Roich, Ron Mokady, Amit\u00a0H. Bermano, and Daniel Cohen-Or. 2021. Pivotal Tuning for Latent-based Editing of Real Images. arXiv.org (2021). https:\/\/arxiv.org\/abs\/2106.05744"},{"key":"e_1_3_2_1_48_1","unstructured":"Chitwan Saharia Jonathan Ho William Chan Tim Salimans David\u00a0J. Fleet and Mohammad Norouzi. 2021. Image Super-Resolution via Iterative Refinement. arxiv.org:2104.07636  Chitwan Saharia Jonathan Ho William Chan Tim Salimans David\u00a0J. Fleet and Mohammad Norouzi. 2021. Image Super-Resolution via Iterative Refinement. arxiv.org:2104.07636"},{"key":"e_1_3_2_1_49_1","unstructured":"Tim Salimans Ian\u00a0J. Goodfellow Wojciech Zaremba Vicki Cheung Alec Radford and Xi Chen. 2016. Improved Techniques for Training GANs. In Advances in Neural Information Processing Systems (NeurIPS). 2226\u20132234. https:\/\/proceedings.neurips.cc\/paper\/2016\/hash\/8a3363abe792db2d8761d6403605aeb7-Abstract.html  Tim Salimans Ian\u00a0J. Goodfellow Wojciech Zaremba Vicki Cheung Alec Radford and Xi Chen. 2016. Improved Techniques for Training GANs. In Advances in Neural Information Processing Systems (NeurIPS). 2226\u20132234. https:\/\/proceedings.neurips.cc\/paper\/2016\/hash\/8a3363abe792db2d8761d6403605aeb7-Abstract.html"},{"key":"e_1_3_2_1_50_1","unstructured":"Axel Sauer Kashyap Chitta Jens M\u00fcller and Andreas Geiger. 2021. Projected GANs Converge Faster. In Advances in Neural Information Processing Systems (NeurIPS).  Axel Sauer Kashyap Chitta Jens M\u00fcller and Andreas Geiger. 2021. Projected GANs Converge Faster. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_1_51_1","volume-title":"Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=BXewfAYMmJw","author":"Sauer Axel","year":"2021","unstructured":"Axel Sauer and Andreas Geiger . 2021 . Counterfactual Generative Networks . In Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=BXewfAYMmJw Axel Sauer and Andreas Geiger. 2021. Counterfactual Generative Networks. In Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=BXewfAYMmJw"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"crossref","first-page":"2004","DOI":"10.1109\/TPAMI.2020.3034267","article-title":"InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs","volume":"44","author":"Shen Yujun","year":"2020","unstructured":"Yujun Shen , Ceyuan Yang , Xiaoou Tang , and Bolei Zhou . 2020 . InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs . IEEE Trans. Pattern Anal. Mach. Intell. 44 , 4 (2020), 2004 \u2013 2018 . https:\/\/doi.org\/10.1109\/TPAMI.2020.3034267 Yujun Shen, Ceyuan Yang, Xiaoou Tang, and Bolei Zhou. 2020. InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs. IEEE Trans. Pattern Anal. Mach. Intell. 44, 4 (2020), 2004\u20132018. https:\/\/doi.org\/10.1109\/TPAMI.2020.3034267","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"e_1_3_2_1_53_1","volume-title":"Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). 1532\u20131540","author":"Shen Yujun","year":"2021","unstructured":"Yujun Shen and Bolei Zhou . 2021 . Closed-Form Factorization of Latent Semantics in GANs . In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). 1532\u20131540 . https:\/\/openaccess.thecvf.com\/content\/CVPR2021\/html\/Shen_Closed-Form_Factorization_of_Latent_Semantics_in_GANs_CVPR_2021_paper.html Yujun Shen and Bolei Zhou. 2021. Closed-Form Factorization of Latent Semantics in GANs. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). 1532\u20131540. https:\/\/openaccess.thecvf.com\/content\/CVPR2021\/html\/Shen_Closed-Form_Factorization_of_Latent_Semantics_in_GANs_CVPR_2021_paper.html"},{"key":"e_1_3_2_1_54_1","volume-title":"Proc. of the International Conf. on Learning Representations (ICLR). https:\/\/openreview.net\/forum?id=St1giarCHLP","author":"Song Jiaming","year":"2021","unstructured":"Jiaming Song , Chenlin Meng , and Stefano Ermon . 2021 . Denoising Diffusion Implicit Models . In Proc. of the International Conf. on Learning Representations (ICLR). https:\/\/openreview.net\/forum?id=St1giarCHLP Jiaming Song, Chenlin Meng, and Stefano Ermon. 2021. Denoising Diffusion Implicit Models. In Proc. of the International Conf. on Learning Representations (ICLR). https:\/\/openreview.net\/forum?id=St1giarCHLP"},{"key":"e_1_3_2_1_55_1","volume-title":"Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=zDy_nQCXiIj","author":"Spingarn Nurit","year":"2021","unstructured":"Nurit Spingarn , Ron Banner , and Tomer Michaeli . 2021 . GAN \u201dSteerability\u201d without optimization . In Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=zDy_nQCXiIj Nurit Spingarn, Ron Banner, and Tomer Michaeli. 2021. GAN \u201dSteerability\u201d without optimization. In Proc. of the International Conf. on Learning Representations (ICLR). OpenReview.net. https:\/\/openreview.net\/forum?id=zDy_nQCXiIj"},{"key":"e_1_3_2_1_56_1","volume-title":"Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, 1\u20139. https:\/\/doi.org\/10","author":"Szegedy Christian","year":"2015","unstructured":"Christian Szegedy , Wei Liu , Yangqing Jia , Pierre Sermanet , Scott\u00a0 E. Reed , Dragomir Anguelov , Dumitru Erhan , Vincent Vanhoucke , and Andrew Rabinovich . 2015 . Going deeper with convolutions . In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, 1\u20139. https:\/\/doi.org\/10 .1109\/CVPR.2015.7298594 Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott\u00a0E. Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, 1\u20139. https:\/\/doi.org\/10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_1_57_1","volume-title":"Proc. of the International Conf. on Machine learning (ICML)(Proceedings of Machine Learning Research, Vol.\u00a097)","author":"Tan Mingxing","year":"2019","unstructured":"Mingxing Tan and Quoc\u00a0 V. Le . 2019 . EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks . In Proc. of the International Conf. on Machine learning (ICML)(Proceedings of Machine Learning Research, Vol.\u00a097) , Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 6105\u20136114. http:\/\/proceedings.mlr.press\/v97\/tan19a.html Mingxing Tan and Quoc\u00a0V. Le. 2019. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In Proc. of the International Conf. on Machine learning (ICML)(Proceedings of Machine Learning Research, Vol.\u00a097), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 6105\u20136114. http:\/\/proceedings.mlr.press\/v97\/tan19a.html"},{"key":"e_1_3_2_1_58_1","unstructured":"Matthew Tancik Pratul\u00a0P. Srinivasan Ben Mildenhall Sara Fridovich-Keil Nithin Raghavan Utkarsh Singhal Ravi Ramamoorthi Jonathan\u00a0T. Barron and Ren Ng. 2020. Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains. In Advances in Neural Information Processing Systems (NeurIPS). https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/55053683268957697aa39fba6f231c68-Abstract.html  Matthew Tancik Pratul\u00a0P. Srinivasan Ben Mildenhall Sara Fridovich-Keil Nithin Raghavan Utkarsh Singhal Ravi Ramamoorthi Jonathan\u00a0T. Barron and Ren Ng. 2020. Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains. In Advances in Neural Information Processing Systems (NeurIPS). https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/55053683268957697aa39fba6f231c68-Abstract.html"},{"key":"e_1_3_2_1_59_1","volume-title":"Proc. of the International Conf. on Machine learning (ICML)(Proceedings of Machine Learning Research, Vol.\u00a0139)","author":"Touvron Hugo","year":"2021","unstructured":"Hugo Touvron , Matthieu Cord , Matthijs Douze , Francisco Massa , Alexandre Sablayrolles , and Herv\u00e9 J\u00e9gou . 2021 . Training data-efficient image transformers & distillation through attention . In Proc. of the International Conf. on Machine learning (ICML)(Proceedings of Machine Learning Research, Vol.\u00a0139) . PMLR, 10347\u201310357. http:\/\/proceedings.mlr.press\/v139\/touvron21a.html Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Herv\u00e9 J\u00e9gou. 2021. Training data-efficient image transformers & distillation through attention. In Proc. of the International Conf. on Machine learning (ICML)(Proceedings of Machine Learning Research, Vol.\u00a0139). PMLR, 10347\u201310357. http:\/\/proceedings.mlr.press\/v139\/touvron21a.html"},{"key":"e_1_3_2_1_60_1","article-title":"Designing an Encoder for StyleGAN Image Manipulation","volume":"40","author":"Tov Omer","year":"2021","unstructured":"Omer Tov , Yuval Alaluf , Yotam Nitzan , Or Patashnik , and Daniel Cohen-Or . 2021 . Designing an Encoder for StyleGAN Image Manipulation . ACM Trans. on Graphics 40 , 4 (2021). https:\/\/doi.org\/10.1145\/3450626.3459838 Omer Tov, Yuval Alaluf, Yotam Nitzan, Or Patashnik, and Daniel Cohen-Or. 2021. Designing an Encoder for StyleGAN Image Manipulation. ACM Trans. on Graphics 40, 4 (2021). https:\/\/doi.org\/10.1145\/3450626.3459838","journal-title":"ACM Trans. on Graphics"},{"key":"e_1_3_2_1_61_1","volume-title":"Advances in Neural Information Processing Systems (NeurIPS), Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna\u00a0M. Wallach, Rob Fergus, S.\u00a0V.\u00a0N","author":"van\u00a0den Oord A\u00e4ron","year":"2017","unstructured":"A\u00e4ron van\u00a0den Oord , Oriol Vinyals , and Koray Kavukcuoglu . 2017. Neural Discrete Representation Learning . In Advances in Neural Information Processing Systems (NeurIPS), Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna\u00a0M. Wallach, Rob Fergus, S.\u00a0V.\u00a0N . Vishwanathan, and Roman Garnett (Eds.). 6306\u20136315. https:\/\/proceedings.neurips.cc\/paper\/ 2017 \/hash\/7a98af17e63a0ac09ce2e96d03992fbc-Abstract.html A\u00e4ron van\u00a0den Oord, Oriol Vinyals, and Koray Kavukcuoglu. 2017. Neural Discrete Representation Learning. In Advances in Neural Information Processing Systems (NeurIPS), Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna\u00a0M. Wallach, Rob Fergus, S.\u00a0V.\u00a0N. Vishwanathan, and Roman Garnett (Eds.). 6306\u20136315. https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/7a98af17e63a0ac09ce2e96d03992fbc-Abstract.html"},{"key":"e_1_3_2_1_62_1","volume-title":"Proc. of the International Conf. on Machine learning (ICML). PMLR, 9786\u20139796","author":"Voynov Andrey","year":"2020","unstructured":"Andrey Voynov and Artem Babenko . 2020 . Unsupervised Discovery of Interpretable Directions in the GAN Latent Space . In Proc. of the International Conf. on Machine learning (ICML). PMLR, 9786\u20139796 . http:\/\/proceedings.mlr.press\/v119\/voynov20a.html Andrey Voynov and Artem Babenko. 2020. Unsupervised Discovery of Interpretable Directions in the GAN Latent Space. In Proc. of the International Conf. on Machine learning (ICML). PMLR, 9786\u20139796. http:\/\/proceedings.mlr.press\/v119\/voynov20a.html"},{"key":"e_1_3_2_1_63_1","volume-title":"Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). Computer Vision Foundation \/ IEEE, 12863\u201312872","author":"Wu Zongze","year":"2021","unstructured":"Zongze Wu , Dani Lischinski , and Eli Shechtman . 2021 . StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation . In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). Computer Vision Foundation \/ IEEE, 12863\u201312872 . https:\/\/openaccess.thecvf.com\/content\/CVPR2021\/html\/Wu_StyleSpace_Analysis_Disentangled_Controls_for_StyleGAN_Image_Generation_CVPR_2021_paper.html Zongze Wu, Dani Lischinski, and Eli Shechtman. 2021. StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). Computer Vision Foundation \/ IEEE, 12863\u201312872. https:\/\/openaccess.thecvf.com\/content\/CVPR2021\/html\/Wu_StyleSpace_Analysis_Disentangled_Controls_for_StyleGAN_Image_Generation_CVPR_2021_paper.html"},{"key":"e_1_3_2_1_64_1","volume-title":"Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). Computer Vision Foundation \/ IEEE, 13569\u201313578","author":"Xu Rui","year":"2021","unstructured":"Rui Xu , Xintao Wang , Kai Chen , Bolei Zhou , and Chen\u00a0Change Loy . 2021 . Positional Encoding As Spatial Inductive Bias in GANs . In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). Computer Vision Foundation \/ IEEE, 13569\u201313578 . https:\/\/openaccess.thecvf.com\/content\/CVPR2021\/html\/Xu_Positional_Encoding_As_Spatial_Inductive_Bias_in_GANs_CVPR_2021_paper.html Rui Xu, Xintao Wang, Kai Chen, Bolei Zhou, and Chen\u00a0Change Loy. 2021. Positional Encoding As Spatial Inductive Bias in GANs. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). Computer Vision Foundation \/ IEEE, 13569\u201313578. https:\/\/openaccess.thecvf.com\/content\/CVPR2021\/html\/Xu_Positional_Encoding_As_Spatial_Inductive_Bias_in_GANs_CVPR_2021_paper.html"},{"key":"e_1_3_2_1_65_1","volume-title":"LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop. arxiv.org:1506.03365","author":"Yu Fisher","year":"2015","unstructured":"Fisher Yu , Yinda Zhang , Shuran Song , Ari Seff , and Jianxiong Xiao . 2015 . LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop. arxiv.org:1506.03365 Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao. 2015. LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop. arxiv.org:1506.03365"},{"key":"e_1_3_2_1_66_1","volume-title":"Proc. of the European Conf. on Computer Vision (ECCV)(Lecture Notes in Computer Science, Vol.\u00a012350)","author":"Zhang Xucong","year":"2020","unstructured":"Xucong Zhang , Seonwook Park , Thabo Beeler , Derek Bradley , Siyu Tang , and Otmar Hilliges . 2020 . ETH-XGaze: A Large Scale Dataset for Gaze Estimation Under Extreme Head Pose and Gaze Variation . In Proc. of the European Conf. on Computer Vision (ECCV)(Lecture Notes in Computer Science, Vol.\u00a012350) . Springer, 365\u2013381. https:\/\/doi.org\/10.1007\/978-3-030-58558-7_22 Xucong Zhang, Seonwook Park, Thabo Beeler, Derek Bradley, Siyu Tang, and Otmar Hilliges. 2020. ETH-XGaze: A Large Scale Dataset for Gaze Estimation Under Extreme Head Pose and Gaze Variation. In Proc. of the European Conf. on Computer Vision (ECCV)(Lecture Notes in Computer Science, Vol.\u00a012350). Springer, 365\u2013381. https:\/\/doi.org\/10.1007\/978-3-030-58558-7_22"},{"key":"e_1_3_2_1_67_1","unstructured":"Shengyu Zhao Zhijian Liu Ji Lin Jun-Yan Zhu and Song Han. 2020. Differentiable Augmentation for Data-Efficient GAN Training. In Advances in Neural Information Processing Systems (NeurIPS). https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/55479c55ebd1efd3ff125f1337100388-Abstract.html  Shengyu Zhao Zhijian Liu Ji Lin Jun-Yan Zhu and Song Han. 2020. Differentiable Augmentation for Data-Efficient GAN Training. In Advances in Neural Information Processing Systems (NeurIPS). https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/55479c55ebd1efd3ff125f1337100388-Abstract.html"},{"key":"e_1_3_2_1_68_1","volume-title":"Proc. of the European Conf. on Computer Vision (ECCV), Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm (Eds.). Springer, 592\u2013608","author":"Zhu Jiapeng","year":"2020","unstructured":"Jiapeng Zhu , Yujun Shen , Deli Zhao , and Bolei Zhou . 2020 . In-Domain GAN Inversion for Real Image Editing . In Proc. of the European Conf. on Computer Vision (ECCV), Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm (Eds.). Springer, 592\u2013608 . https:\/\/doi.org\/10.1007\/978-3-030-58520-4_35 Jiapeng Zhu, Yujun Shen, Deli Zhao, and Bolei Zhou. 2020. In-Domain GAN Inversion for Real Image Editing. In Proc. of the European Conf. on Computer Vision (ECCV), Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm (Eds.). Springer, 592\u2013608. https:\/\/doi.org\/10.1007\/978-3-030-58520-4_35"}],"event":{"name":"SIGGRAPH '22: Special Interest Group on Computer Graphics and Interactive Techniques Conference","location":"Vancouver BC Canada","acronym":"SIGGRAPH '22","sponsor":["SIGGRAPH ACM Special Interest Group on Computer Graphics and Interactive Techniques"]},"container-title":["Special Interest Group on Computer Graphics and Interactive Techniques Conference Proceedings"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3528233.3530738","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:42Z","timestamp":1750186962000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3528233.3530738"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,7]]},"references-count":68,"alternative-id":["10.1145\/3528233.3530738","10.1145\/3528233"],"URL":"https:\/\/doi.org\/10.1145\/3528233.3530738","relation":{},"subject":[],"published":{"date-parts":[[2022,8,7]]}}}