{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T22:31:15Z","timestamp":1757543475790,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":57,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T00:00:00Z","timestamp":1602460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Natural Science Foundation of China","award":["No. 61876007"],"award-info":[{"award-number":["No. 61876007"]}]},{"name":"Australian Research Council","award":["DE180101438"],"award-info":[{"award-number":["DE180101438"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,10,12]]},"DOI":"10.1145\/3394171.3413968","type":"proceedings-article","created":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T13:12:00Z","timestamp":1602508320000},"page":"1561-1569","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Discernible Image Compression"],"prefix":"10.1145","author":[{"given":"Zhaohui","family":"Yang","sequence":"first","affiliation":[{"name":"Peking University & Noah's Ark Lab, Huawei Technologies, Beijing, China"}]},{"given":"Yunhe","family":"Wang","sequence":"additional","affiliation":[{"name":"Noah's Ark Lab, Huawei Technologies, Beijing, China"}]},{"given":"Chang","family":"Xu","sequence":"additional","affiliation":[{"name":"University of Sydney, Sydney, Australia"}]},{"given":"Peng","family":"Du","sequence":"additional","affiliation":[{"name":"Huawei Technologies, Beijing, China"}]},{"given":"Chao","family":"Xu","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}]},{"given":"Chunjing","family":"Xu","sequence":"additional","affiliation":[{"name":"Noah's Ark Lab, Huawei Technologies, Beijing, China"}]},{"given":"Qi","family":"Tian","sequence":"additional","affiliation":[{"name":"Coud & AI, Huawei Technologies, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2020,10,12]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Learning-Based Image Compression using Convolutional Autoencoder and Wavelet Decomposition. CVPRW","author":"Akyazi Pinar","year":"2019","unstructured":"Pinar Akyazi and Touradj Ebrahimi . 2019. Learning-Based Image Compression using Convolutional Autoencoder and Wavelet Decomposition. CVPRW ( 2019 ). Pinar Akyazi and Touradj Ebrahimi. 2019. Learning-Based Image Compression using Convolutional Autoencoder and Wavelet Decomposition. CVPRW (2019)."},{"key":"e_1_3_2_2_2_1","volume-title":"Learning to inpaint for image compression. NIPS","author":"Baig Mohammad Haris","year":"2017","unstructured":"Mohammad Haris Baig , Vladlen Koltun , and Lorenzo Torresani . 2017. Learning to inpaint for image compression. NIPS ( 2017 ), 1246--1255. Mohammad Haris Baig, Vladlen Koltun, and Lorenzo Torresani. 2017. Learning to inpaint for image compression. NIPS (2017), 1246--1255."},{"key":"e_1_3_2_2_3_1","volume-title":"End-to-end optimized image compression. arXiv preprint arXiv:1611.01704","author":"Ball\u00e9 Johannes","year":"2016","unstructured":"Johannes Ball\u00e9 , Valero Laparra , and Eero P Simoncelli . 2016. End-to-end optimized image compression. arXiv preprint arXiv:1611.01704 ( 2016 ). Johannes Ball\u00e9, Valero Laparra, and Eero P Simoncelli. 2016. End-to-end optimized image compression. arXiv preprint arXiv:1611.01704 (2016)."},{"key":"e_1_3_2_2_4_1","volume-title":"Image denoising: Can plain neural networks compete with BM3D? CVPR","author":"Burger Harold C","year":"2012","unstructured":"Harold C Burger , Christian J Schuler , and Stefan Harmeling . 2012. Image denoising: Can plain neural networks compete with BM3D? CVPR ( 2012 ). Harold C Burger, Christian J Schuler, and Stefan Harmeling. 2012. Image denoising: Can plain neural networks compete with BM3D? CVPR (2012)."},{"key":"e_1_3_2_2_5_1","volume-title":"Efficient Learning Based Sub-pixel Image Compression. CVPR","author":"Cai Chunlei","year":"2019","unstructured":"Chunlei Cai , Guo Lu , Qiang Hu , Li Chen , and Zhiyong Gao . 2019. Efficient Learning Based Sub-pixel Image Compression. CVPR ( 2019 ). Chunlei Cai, Guo Lu, Qiang Hu, Li Chen, and Zhiyong Gao. 2019. Efficient Learning Based Sub-pixel Image Compression. CVPR (2019)."},{"key":"e_1_3_2_2_6_1","volume-title":"Data-Free Learning of Student Networks. ICCV","author":"Chen Hanting","year":"2019","unstructured":"Hanting Chen , Yunhe Wang , Chang Xu , Zhaohui Yang , Chuanjian Liu , Boxin Shi , Chunjing Xu , Chao Xu , and Qi Tian . 2019. Data-Free Learning of Student Networks. ICCV ( 2019 ). Hanting Chen, Yunhe Wang, Chang Xu, Zhaohui Yang, Chuanjian Liu, Boxin Shi, Chunjing Xu, Chao Xu, and Qi Tian. 2019. Data-Free Learning of Student Networks. ICCV (2019)."},{"key":"e_1_3_2_2_7_1","volume-title":"2019 a. Deep Residual Learning for Image Compression. CVPRW","author":"Cheng Zhengxue","year":"2019","unstructured":"Zhengxue Cheng , Heming Sun , Masaru Takeuchi , and Jiro Katto . 2019 a. Deep Residual Learning for Image Compression. CVPRW ( 2019 ). Zhengxue Cheng, Heming Sun, Masaru Takeuchi, and Jiro Katto. 2019 a. Deep Residual Learning for Image Compression. CVPRW (2019)."},{"key":"e_1_3_2_2_8_1","volume-title":"2019 b. Learning Image and Video Compression Through Spatial-Temporal Energy Compaction. CVPR","author":"Cheng Zhengxue","year":"2019","unstructured":"Zhengxue Cheng , Heming Sun , Masaru Takeuchi , and Jiro Katto . 2019 b. Learning Image and Video Compression Through Spatial-Temporal Energy Compaction. CVPR ( 2019 ). Zhengxue Cheng, Heming Sun, Masaru Takeuchi, and Jiro Katto. 2019 b. Learning Image and Video Compression Through Spatial-Temporal Energy Compaction. CVPR (2019)."},{"key":"e_1_3_2_2_9_1","volume-title":"Variable Rate Deep Image Compression With a Conditional Autoencoder. ICCV","author":"Choi Yoojin","year":"2019","unstructured":"Yoojin Choi , Mostafa El-Khamy , and Jungwon Lee . 2019. Variable Rate Deep Image Compression With a Conditional Autoencoder. ICCV ( 2019 ). Yoojin Choi, Mostafa El-Khamy, and Jungwon Lee. 2019. Variable Rate Deep Image Compression With a Conditional Autoencoder. ICCV (2019)."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3240508.3240706"},{"key":"e_1_3_2_2_11_1","volume-title":"Chen Change Loy, and Xiaoou Tang","author":"Dong Chao","year":"2015","unstructured":"Chao Dong , Yubin Deng , Chen Change Loy, and Xiaoou Tang . 2015 . Compression artifacts reduction by a deep convolutional network. ICCV ( 2015). Chao Dong, Yubin Deng, Chen Change Loy, and Xiaoou Tang. 2015. Compression artifacts reduction by a deep convolutional network. ICCV (2015)."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2439281"},{"key":"e_1_3_2_2_13_1","volume-title":"Centralized sparse representation for image restoration. ICCV","author":"Dong Weisheng","year":"2011","unstructured":"Weisheng Dong , Lei Zhang , and Guangming Shi . 2011. Centralized sparse representation for image restoration. ICCV ( 2011 ), 1259--1266. Weisheng Dong, Lei Zhang, and Guangming Shi. 2011. Centralized sparse representation for image restoration. ICCV (2011), 1259--1266."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"},{"key":"e_1_3_2_2_15_1","volume-title":"Rich feature hierarchies for accurate object detection and semantic segmentation. CVPR","author":"Girshick Ross","year":"2014","unstructured":"Ross Girshick , Jeff Donahue , Trevor Darrell , and Jitendra Malik . 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. CVPR ( 2014 ). Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. CVPR (2014)."},{"key":"e_1_3_2_2_16_1","volume-title":"Weighted nuclear norm minimization with application to image denoising. CVPR","author":"Gu Shuhang","year":"2014","unstructured":"Shuhang Gu , Lei Zhang , Wangmeng Zuo , and Xiangchu Feng . 2014. Weighted nuclear norm minimization with application to image denoising. CVPR ( 2014 ). Shuhang Gu, Lei Zhang, Wangmeng Zuo, and Xiangchu Feng. 2014. Weighted nuclear norm minimization with application to image denoising. CVPR (2014)."},{"key":"e_1_3_2_2_17_1","volume-title":"Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection. CVPR","author":"Guo Jianyuan","year":"2020","unstructured":"Jianyuan Guo , Kai Han , Yunhe Wang , Chao Zhang , Zhaohui Yang , Han Wu , Xinghao Chen , and Chang Xu. 2020. Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection. CVPR ( 2020 ). Jianyuan Guo, Kai Han, Yunhe Wang, Chao Zhang, Zhaohui Yang, Han Wu, Xinghao Chen, and Chang Xu. 2020. Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection. CVPR (2020)."},{"key":"e_1_3_2_2_18_1","volume-title":"Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385","author":"He Kaiming","year":"2015","unstructured":"Kaiming He , Xiangyu Zhang , Shaoqing Ren , and Jian Sun . 2015. Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385 ( 2015 ). Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385 (2015)."},{"key":"e_1_3_2_2_19_1","volume-title":"Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531","author":"Hinton Geoffrey","year":"2015","unstructured":"Geoffrey Hinton , Oriol Vinyals , and Jeff Dean . 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 ( 2015 ). Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)."},{"key":"e_1_3_2_2_20_1","volume-title":"ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples. CVPR","author":"Jia Xiaojun","year":"2019","unstructured":"Xiaojun Jia , Xingxing Wei , Xiaochun Cao , and Hassan Foroosh . 2019. ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples. CVPR ( 2019 ). Xiaojun Jia, Xingxing Wei, Xiaochun Cao, and Hassan Foroosh. 2019. ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples. CVPR (2019)."},{"key":"e_1_3_2_2_21_1","volume-title":"An End-to-End Compression Framework Based on Convolutional Neural Networks","author":"Jiang Feng","year":"2017","unstructured":"Feng Jiang , Wen Tao , Shaohui Liu , Jie Ren , Xun Guo , and Debin Zhao . 2017. An End-to-End Compression Framework Based on Convolutional Neural Networks . IEEE TCSVT ( 2017 ). Feng Jiang, Wen Tao, Shaohui Liu, Jie Ren, Xun Guo, and Debin Zhao. 2017. An End-to-End Compression Framework Based on Convolutional Neural Networks. IEEE TCSVT (2017)."},{"key":"e_1_3_2_2_22_1","volume-title":"Context-adaptive Entropy Model for End-to-end Optimized Image Compression. ICLR","author":"Lee Jooyoung","year":"2019","unstructured":"Jooyoung Lee , Seunghyun Cho , and Seung-Kwon Beack . 2019. Context-adaptive Entropy Model for End-to-end Optimized Image Compression. ICLR ( 2019 ). Jooyoung Lee, Seunghyun Cho, and Seung-Kwon Beack. 2019. Context-adaptive Entropy Model for End-to-end Optimized Image Compression. ICLR (2019)."},{"key":"e_1_3_2_2_23_1","volume-title":"Learning Convolutional Networks for Content-weighted Image Compression. CVPR","author":"Li Mu","year":"2018","unstructured":"Mu Li , Wangmeng Zuo , Shuhang Gu , Debin Zhao , and David Zhang . 2018. Learning Convolutional Networks for Content-weighted Image Compression. CVPR ( 2018 ). Mu Li, Wangmeng Zuo, Shuhang Gu, Debin Zhao, and David Zhang. 2018. Learning Convolutional Networks for Content-weighted Image Compression. CVPR (2018)."},{"key":"e_1_3_2_2_24_1","volume-title":"Microsoft coco: Common objects in context. ECCV","author":"Lin Tsung-Yi","year":"2014","unstructured":"Tsung-Yi Lin , Michael Maire , Serge Belongie , James Hays , Pietro Perona , Deva Ramanan , Piotr Doll\u00e1r , and C Lawrence Zitnick . 2014. Microsoft coco: Common objects in context. ECCV ( 2014 ). Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll\u00e1r, and C Lawrence Zitnick. 2014. Microsoft coco: Common objects in context. ECCV (2014)."},{"key":"e_1_3_2_2_25_1","volume-title":"Ssd: Single shot multibox detector. ECCV","author":"Liu Wei","year":"2016","unstructured":"Wei Liu , Dragomir Anguelov , Dumitru Erhan , Christian Szegedy , Scott Reed , Cheng-Yang Fu , and Alexander C Berg . 2016 . Ssd: Single shot multibox detector. ECCV (2016). Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg. 2016. Ssd: Single shot multibox detector. ECCV (2016)."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3195970.3196022"},{"key":"e_1_3_2_2_27_1","volume-title":"Fully convolutional networks for semantic segmentation. CVPR","author":"Long Jonathan","year":"2015","unstructured":"Jonathan Long , Evan Shelhamer , and Trevor Darrell . 2015b. Fully convolutional networks for semantic segmentation. CVPR ( 2015 ). Jonathan Long, Evan Shelhamer, and Trevor Darrell. 2015b. Fully convolutional networks for semantic segmentation. CVPR (2015)."},{"key":"e_1_3_2_2_28_1","volume-title":"Learning transferable features with deep adaptation networks. ICML","author":"Long Mingsheng","year":"2015","unstructured":"Mingsheng Long , Yue Cao , Jianmin Wang , and Michael Jordan . 2015a. Learning transferable features with deep adaptation networks. ICML ( 2015 ). Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan. 2015a. Learning transferable features with deep adaptation networks. ICML (2015)."},{"key":"e_1_3_2_2_29_1","unstructured":"Ping Luo Zhenyao Zhu Ziwei Liu Xiaogang Wang Xiaoou Tang etal 2016. Face Model Compression by Distilling Knowledge from Neurons. AAAI (2016) 3560--3566.  Ping Luo Zhenyao Zhu Ziwei Liu Xiaogang Wang Xiaoou Tang et al. 2016. Face Model Compression by Distilling Knowledge from Neurons. AAAI (2016) 3560--3566."},{"key":"e_1_3_2_2_30_1","volume-title":"Practical Full Resolution Learned Lossless Image Compression. CVPR","author":"Mentzer Fabian","year":"2019","unstructured":"Fabian Mentzer , Eirikur Agustsson , Michael Tschannen , Radu Timofte , and Luc Van Gool . 2019. Practical Full Resolution Learned Lossless Image Compression. CVPR ( 2019 ). Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, and Luc Van Gool. 2019. Practical Full Resolution Learned Lossless Image Compression. CVPR (2019)."},{"key":"e_1_3_2_2_31_1","volume-title":"Conditional probability models for deep image compression. CVPR","author":"Mentzer Fabian","year":"2018","unstructured":"Fabian Mentzer , Eirikur Agustsson , Michael Tschannen , Radu Timofte , and Luc Van Gool . 2018. Conditional probability models for deep image compression. CVPR ( 2018 ). Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, and Luc Van Gool. 2018. Conditional probability models for deep image compression. CVPR (2018)."},{"key":"e_1_3_2_2_32_1","unstructured":"David Minnen Johannes Ball\u00e9 and George D Toderici. [n.d.]. Joint Autoregressive and Hierarchical Priors for Learned Image Compression. In NeurIPS .  David Minnen Johannes Ball\u00e9 and George D Toderici. [n.d.]. Joint Autoregressive and Hierarchical Priors for Learned Image Compression. In NeurIPS ."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/2964284.2967305"},{"key":"e_1_3_2_2_34_1","volume-title":"Pixel recurrent neural networks. arXiv preprint arXiv:1601.06759","author":"van den Oord Aaron","year":"2016","unstructured":"Aaron van den Oord , Nal Kalchbrenner , and Koray Kavukcuoglu . 2016. Pixel recurrent neural networks. arXiv preprint arXiv:1601.06759 ( 2016 ). Aaron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. 2016. Pixel recurrent neural networks. arXiv preprint arXiv:1601.06759 (2016)."},{"key":"e_1_3_2_2_35_1","volume-title":"Faster R-CNN: Towards real-time object detection with region proposal networks. NIPS","author":"Ren Shaoqing","year":"2015","unstructured":"Shaoqing Ren , Kaiming He , Ross Girshick , and Jian Sun . 2015. Faster R-CNN: Towards real-time object detection with region proposal networks. NIPS ( 2015 ). Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015. Faster R-CNN: Towards real-time object detection with region proposal networks. NIPS (2015)."},{"key":"e_1_3_2_2_36_1","volume-title":"Real-Time Adaptive Image Compression. arXiv preprint arXiv:1705.05823","author":"Rippel Oren","year":"2017","unstructured":"Oren Rippel and Lubomir Bourdev . 2017. Real-Time Adaptive Image Compression. arXiv preprint arXiv:1705.05823 ( 2017 ). Oren Rippel and Lubomir Bourdev. 2017. Real-Time Adaptive Image Compression. arXiv preprint arXiv:1705.05823 (2017)."},{"key":"e_1_3_2_2_37_1","volume-title":"Antoine Chassang, Carlo Gatta, and Yoshua Bengio.","author":"Romero Adriana","year":"2014","unstructured":"Adriana Romero , Nicolas Ballas , Samira Ebrahimi Kahou , Antoine Chassang, Carlo Gatta, and Yoshua Bengio. 2014 . Fitnets : Hints for thin deep nets. arXiv preprint arXiv:1412.6550 (2014). Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio. 2014. Fitnets: Hints for thin deep nets. arXiv preprint arXiv:1412.6550 (2014)."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_3_2_2_39_1","volume-title":"A Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image Compression. CVPRW","author":"Savioli Nicol\u00f3","year":"2019","unstructured":"Nicol\u00f3 Savioli . 2019. A Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image Compression. CVPRW ( 2019 ). Nicol\u00f3 Savioli. 2019. A Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image Compression. CVPRW (2019)."},{"key":"e_1_3_2_2_40_1","volume-title":"Equivalence of distance-based and RKHS-based statistics in hypothesis testing. The Annals of Statistics","author":"Sejdinovic Dino","year":"2013","unstructured":"Dino Sejdinovic , Bharath Sriperumbudur , Arthur Gretton , and Kenji Fukumizu . 2013. Equivalence of distance-based and RKHS-based statistics in hypothesis testing. The Annals of Statistics ( 2013 ), 2263--2291. Dino Sejdinovic, Bharath Sriperumbudur, Arthur Gretton, and Kenji Fukumizu. 2013. Equivalence of distance-based and RKHS-based statistics in hypothesis testing. The Annals of Statistics (2013), 2263--2291."},{"key":"e_1_3_2_2_41_1","volume-title":"Very deep convolutional networks for large-scale image recognition. ICLR","author":"Simonyan Karen","year":"2015","unstructured":"Karen Simonyan and Andrew Zisserman . 2015. Very deep convolutional networks for large-scale image recognition. ICLR ( 2015 ). Karen Simonyan and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. ICLR (2015)."},{"key":"e_1_3_2_2_42_1","volume-title":"The JPEG 2000 still image compression standard","author":"Skodras Athanassios","year":"2001","unstructured":"Athanassios Skodras , Charilaos Christopoulos , and Touradj Ebrahimi . 2001. The JPEG 2000 still image compression standard . IEEE Signal processing magazine, Vol. 18 , 5 ( 2001 ), 36--58. Athanassios Skodras, Charilaos Christopoulos, and Touradj Ebrahimi. 2001. The JPEG 2000 still image compression standard. IEEE Signal processing magazine, Vol. 18, 5 (2001), 36--58."},{"key":"e_1_3_2_2_43_1","volume-title":"Efficient Residual Dense Block Search for Image Super-Resolution. AAAI","author":"Song Dehua","year":"2020","unstructured":"Dehua Song , Chang Xu , Xu Jia , Yiyi Chen , Chunjing Xu , and Yunhe Wang . 2020. Efficient Residual Dense Block Search for Image Super-Resolution. AAAI ( 2020 ). Dehua Song, Chang Xu, Xu Jia, Yiyi Chen, Chunjing Xu, and Yunhe Wang. 2020. Efficient Residual Dense Block Search for Image Super-Resolution. AAAI (2020)."},{"key":"e_1_3_2_2_44_1","volume-title":"Compressed Sensing MRI Using a Recursive Dilated Network. AAAI","author":"Sun Liyan","year":"2018","unstructured":"Liyan Sun , Zhiwen Fan , Yue Huang , Xinghao Ding , and John Paisley . 2018. Compressed Sensing MRI Using a Recursive Dilated Network. AAAI ( 2018 ). Liyan Sun, Zhiwen Fan, Yue Huang, Xinghao Ding, and John Paisley. 2018. Compressed Sensing MRI Using a Recursive Dilated Network. AAAI (2018)."},{"key":"e_1_3_2_2_45_1","volume-title":"Deep learning face representation by joint identification-verification. NIPS","author":"Sun Yi","year":"2014","unstructured":"Yi Sun , Yuheng Chen , Xiaogang Wang , and Xiaoou Tang . 2014. Deep learning face representation by joint identification-verification. NIPS ( 2014 ). Yi Sun, Yuheng Chen, Xiaogang Wang, and Xiaoou Tang. 2014. Deep learning face representation by joint identification-verification. NIPS (2014)."},{"key":"e_1_3_2_2_46_1","volume-title":"Generative image modeling using spatial LSTMs. NIPS","author":"Theis Lucas","year":"2015","unstructured":"Lucas Theis and Matthias Bethge . 2015. Generative image modeling using spatial LSTMs. NIPS ( 2015 ). Lucas Theis and Matthias Bethge. 2015. Generative image modeling using spatial LSTMs. NIPS (2015)."},{"key":"e_1_3_2_2_47_1","volume-title":"Lossy image compression with compressive autoencoders. arXiv preprint arXiv:1703.00395","author":"Theis Lucas","year":"2017","unstructured":"Lucas Theis , Wenzhe Shi , Andrew Cunningham , and Ferenc Husz\u00e1r . 2017. Lossy image compression with compressive autoencoders. arXiv preprint arXiv:1703.00395 ( 2017 ). Lucas Theis, Wenzhe Shi, Andrew Cunningham, and Ferenc Husz\u00e1r. 2017. Lossy image compression with compressive autoencoders. arXiv preprint arXiv:1703.00395 (2017)."},{"key":"e_1_3_2_2_48_1","volume-title":"Damien Vincent, David Minnen, Shumeet Baluja, Michele Covell, and Rahul Sukthankar.","author":"Toderici George","year":"2015","unstructured":"George Toderici , Sean M O'Malley , Sung Jin Hwang , Damien Vincent, David Minnen, Shumeet Baluja, Michele Covell, and Rahul Sukthankar. 2015 . Variable rate image compression with recurrent neural networks. arXiv preprint arXiv:1511.06085 (2015). George Toderici, Sean M O'Malley, Sung Jin Hwang, Damien Vincent, David Minnen, Shumeet Baluja, Michele Covell, and Rahul Sukthankar. 2015. Variable rate image compression with recurrent neural networks. arXiv preprint arXiv:1511.06085 (2015)."},{"key":"e_1_3_2_2_49_1","volume-title":"David Minnen, Joel Shor, and Michele Covell.","author":"Toderici George","year":"2017","unstructured":"George Toderici , Damien Vincent , Nick Johnston , Sung Jin Hwang , David Minnen, Joel Shor, and Michele Covell. 2017 . Full Resolution Image Compression with Recurrent Neural Networks. CVPR ( 2017), 5435--5443. George Toderici, Damien Vincent, Nick Johnston, Sung Jin Hwang, David Minnen, Joel Shor, and Michele Covell. 2017. Full Resolution Image Compression with Recurrent Neural Networks. CVPR (2017), 5435--5443."},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/30.125072"},{"key":"e_1_3_2_2_51_1","volume-title":"Systems and Computers, 2004. Conference Record of the Thirty-Seventh Asilomar Conference on","author":"Wang Zhou","year":"2003","unstructured":"Zhou Wang , Eero P Simoncelli , and Alan C Bovik . 2003 . Multiscale structural similarity for image quality assessment. Signals , Systems and Computers, 2004. Conference Record of the Thirty-Seventh Asilomar Conference on (2003). Zhou Wang, Eero P Simoncelli, and Alan C Bovik. 2003. Multiscale structural similarity for image quality assessment. Signals, Systems and Computers, 2004. Conference Record of the Thirty-Seventh Asilomar Conference on (2003)."},{"key":"e_1_3_2_2_52_1","volume-title":"Image denoising and inpainting with deep neural networks. NIPS","author":"Xie Junyuan","year":"2012","unstructured":"Junyuan Xie , Linli Xu , and Enhong Chen . 2012. Image denoising and inpainting with deep neural networks. NIPS ( 2012 ). Junyuan Xie, Linli Xu, and Enhong Chen. 2012. Image denoising and inpainting with deep neural networks. NIPS (2012)."},{"key":"e_1_3_2_2_53_1","volume-title":"2020 a. CARS: Continuous Evolution for Efficient Neural Architecture Search. CVPR","author":"Yang Zhaohui","year":"2020","unstructured":"Zhaohui Yang , Yunhe Wang , Xinghao Chen , Boxin Shi , Chao Xu , Chunjing Xu , Qi Tian , and Chang Xu . 2020 a. CARS: Continuous Evolution for Efficient Neural Architecture Search. CVPR ( 2020 ). Zhaohui Yang, Yunhe Wang, Xinghao Chen, Boxin Shi, Chao Xu, Chunjing Xu, Qi Tian, and Chang Xu. 2020 a. CARS: Continuous Evolution for Efficient Neural Architecture Search. CVPR (2020)."},{"key":"e_1_3_2_2_54_1","volume-title":"LegoNet: Efficient Convolutional Neural Networks with Lego Filters. ICML","author":"Yang Zhaohui","year":"2019","unstructured":"Zhaohui Yang , Yunhe Wang , Chuanjian Liu , Hanting Chen , Chunjing Xu , Boxin Shi , Chao Xu , and Chang Xu. 2019. LegoNet: Efficient Convolutional Neural Networks with Lego Filters. ICML ( 2019 ). Zhaohui Yang, Yunhe Wang, Chuanjian Liu, Hanting Chen, Chunjing Xu, Boxin Shi, Chao Xu, and Chang Xu. 2019. LegoNet: Efficient Convolutional Neural Networks with Lego Filters. ICML (2019)."},{"key":"e_1_3_2_2_55_1","volume-title":"2020 b. HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens. arXiv preprint arXiv:2005.14446","author":"Yang Zhaohui","year":"2020","unstructured":"Zhaohui Yang , Yunhe Wang , Dacheng Tao , Xinghao Chen , Jianyuan Guo , Chunjing Xu , Chao Xu , and Chang Xu . 2020 b. HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens. arXiv preprint arXiv:2005.14446 ( 2020 ). Zhaohui Yang, Yunhe Wang, Dacheng Tao, Xinghao Chen, Jianyuan Guo, Chunjing Xu, Chao Xu, and Chang Xu. 2020 b. HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens. arXiv preprint arXiv:2005.14446 (2020)."},{"key":"e_1_3_2_2_56_1","volume-title":"Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising","author":"Zhang Kai","year":"2017","unstructured":"Kai Zhang , Wangmeng Zuo , Yunjin Chen , Deyu Meng , and Lei Zhang . 2017. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising . IEEE TIP ( 2017 ). Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang. 2017. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE TIP (2017)."},{"key":"e_1_3_2_2_57_1","volume-title":"Hannuksela","author":"Aytekin \u00c7aglar","year":"2019","unstructured":"\u00c7aglar Aytekin , Francesco Cricri , Antti Hallapuro , Jani Lainema , Emre Aksu , and Miska M . Hannuksela . 2019 . A Compression Objective and a Cycle Loss for Neural Image Compression. CVPRW ( 2019). \u00c7aglar Aytekin, Francesco Cricri, Antti Hallapuro, Jani Lainema, Emre Aksu, and Miska M. Hannuksela. 2019. A Compression Objective and a Cycle Loss for Neural Image Compression. CVPRW (2019)."}],"event":{"name":"MM '20: The 28th ACM International Conference on Multimedia","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Seattle WA USA","acronym":"MM '20"},"container-title":["Proceedings of the 28th ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394171.3413968","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3394171.3413968","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:32:07Z","timestamp":1750195927000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394171.3413968"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,12]]},"references-count":57,"alternative-id":["10.1145\/3394171.3413968","10.1145\/3394171"],"URL":"https:\/\/doi.org\/10.1145\/3394171.3413968","relation":{},"subject":[],"published":{"date-parts":[[2020,10,12]]},"assertion":[{"value":"2020-10-12","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}