{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:51:25Z","timestamp":1777657885475,"version":"3.51.4"},"reference-count":65,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T00:00:00Z","timestamp":1692748800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key R &amp; D projects in Hubei Province","award":["2021BAA188"],"award-info":[{"award-number":["2021BAA188"]}]},{"name":"Key R &amp; D projects in Hubei Province","award":["22S04"],"award-info":[{"award-number":["22S04"]}]},{"name":"Open Research Fund Program of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University","award":["2021BAA188"],"award-info":[{"award-number":["2021BAA188"]}]},{"name":"Open Research Fund Program of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University","award":["22S04"],"award-info":[{"award-number":["22S04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As one of the representative models in the field of image generation, generative adversarial networks (GANs) face a significant challenge: how to make the best trade-off between the quality of generated images and training stability. The U-Net based GAN (U-Net GAN), a recently developed approach, can generate high-quality synthetic images by using a U-Net architecture for the discriminator. However, this model may suffer from severe mode collapse. In this study, a stable U-Net GAN (SUGAN) is proposed to mainly solve this problem. First, a gradient normalization module is introduced to the discriminator of U-Net GAN. This module effectively reduces gradient magnitudes, thereby greatly alleviating the problems of gradient instability and overfitting. As a result, the training stability of the GAN model is improved. Additionally, in order to solve the problem of blurred edges of the generated images, a modified residual network is used in the generator. This modification enhances its ability to capture image details, leading to higher-definition generated images. Extensive experiments conducted on several datasets show that the proposed SUGAN significantly improves over the Inception Score (IS) and Fr\u00e9chet Inception Distance (FID) metrics compared with several state-of-the-art and classic GANs. The training process of our SUGAN is stable, and the quality and diversity of the generated samples are higher. This clearly demonstrates the effectiveness of our approach for image generation tasks. The source code and trained model of our SUGAN have been publicly released.<\/jats:p>","DOI":"10.3390\/s23177338","type":"journal-article","created":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T08:20:30Z","timestamp":1692778830000},"page":"7338","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["SUGAN: A Stable U-Net Based Generative Adversarial Network"],"prefix":"10.3390","volume":"23","author":[{"given":"Shijie","family":"Cheng","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Hubei University, Wuhan 430062, China"},{"name":"School of Computer Science and Information Engineering, Hubei University, Wuhan 430062, China"},{"name":"Key Laboratory of Intelligent Sensing System and Security (Hubei University), Ministry of Education, Wuhan 430062, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingfeng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Engineering, Hubei University, Wuhan 430062, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1643-5271","authenticated-orcid":false,"given":"Min","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Hubei University, Wuhan 430062, China"},{"name":"Key Laboratory of Intelligent Sensing System and Security (Hubei University), Ministry of Education, Wuhan 430062, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Hubei University, Wuhan 430062, China"},{"name":"Key Laboratory of Intelligent Sensing System and Security (Hubei University), Ministry of Education, Wuhan 430062, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,23]]},"reference":[{"key":"ref_1","unstructured":"Ho, J., Jain, A., and Abbeel, P. (2020, January 6\u201312). Denoising diffusion probabilistic models. Proceedings of the 34th International Conference on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_2","unstructured":"Song, J., Meng, C., and Ermon, S. (2021, January 3\u20137). Denoising diffusion implicit models. Proceedings of the International Conference on Learning Representations, Virtual."},{"key":"ref_3","unstructured":"Dhariwal, P., and Nichol, A. (2021, January 6\u201314). Diffusion models beat gans on image synthesis. Proceedings of the 35th International Conference on Neural Information Processing Systems, Virtual."},{"key":"ref_4","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 23\u201327). Generative adversarial nets. Proceedings of the 27th International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_5","unstructured":"Karras, T., Aittala, M., Laine, S., H\u00e4rk\u00f6nen, E., Hellsten, J., Lehtinen, J., and Aila, T. (2021, January 6\u201314). Alias-free generative adversarial networks. Proceedings of the 35th International Conference on Neural Information Processing Systems, Virtual."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., and Aila, T. (2020, January 13\u201319). Analyzing and improving the image quality of stylegan. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dai, M., Hang, H., and Guo, X. (2022, January 23\u201327). Adaptive Feature Interpolation for Low-Shot Image Generation. Proceedings of the Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-19784-0_15"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kim, J., Choi, Y., and Uh, Y. (2022, January 18\u201324). Feature statistics mixing regularization for generative adversarial networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01101"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., and Wang, Z. (2017, January 21\u201326). Photo-realistic single image super-resolution using a generative adversarial network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"He, J., Shi, W., Chen, K., Fu, L., and Dong, C. (2022, January 18\u201324). Gcfsr: A generative and controllable face super resolution method without facial and gan priors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00193"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liang, J., Zeng, H., and Zhang, L. (2022, January 18\u201324). Details or artifacts: A locally discriminative learning approach to realistic image super-resolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00557"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chen, Y.-I., Chang, Y.-j., Sun, Y., Liao, S.-C., Santacruz, S.R., and Yeh, H.-C. (November, January 31). Generative adversarial network improves the resolution of pulsed STED microscopy. Proceedings of the 2022 56th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA.","DOI":"10.1109\/IEEECONF56349.2022.10051896"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Qiao, T., Zhang, J., Xu, D., and Tao, D. (2019, January 15\u201320). Mirrorgan: Learning text-to-image generation by redescription. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00160"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, M., Li, R., Jia, K., and Zhang, L. (2022, January 18\u201324). Exact feature distribution matching for arbitrary style transfer and domain generalization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00787"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tao, T., Zhan, X., Chen, Z., and van de Panne, M. (2022, January 18\u201324). Style-ERD: Responsive and coherent online motion style transfer. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00648"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yang, S., Jiang, L., Liu, Z., and Loy, C.C. (2022, January 18\u201324). Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00754"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Dalva, Y., Alt\u0131ndi\u015f, S.F., and Dundar, A. (2022, January 23\u201327). Vecgan: Image-to-image translation with interpretable latent directions. Proceedings of the Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-19787-1_9"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, S., Ye, J., Ren, S., and Wang, X. (2022, January 23\u201327). Dynast: Dynamic sparse transformer for exemplar-guided image generation. Proceedings of the Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-19787-1_5"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Couairon, G., Grechka, A., Verbeek, J., Schwenk, H., and Cord, M. (2022, January 18\u201324). Flexit: Towards flexible semantic image translation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01773"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Choi, Y., Choi, M., Kim, M., Ha, J.-W., Kim, S., and Choo, J. (2018, January 18\u201323). Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00916"},{"key":"ref_21","unstructured":"Kim, H., Jhoo, H.Y., Park, E., and Yoo, S. (November, January 27). Tag2pix: Line art colorization using text tag with secat and changing loss. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_22","unstructured":"Kim, J., Kim, M., Kang, H., and Lee, K. (2020, January 26\u201330). U-gat-it: Unsupervised generative attentional networks with adaptive layer-instance normalization for image-to-image translation. Proceedings of the International Conference on Learning Representations, Ababa, Ethiopia."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.comcom.2022.12.001","article-title":"Sec-edge: Trusted blockchain system for enabling the identification and authentication of edge based 5G networks","volume":"199","author":"Babu","year":"2023","journal-title":"Comput. Commun."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"126391","DOI":"10.1016\/j.neucom.2023.126391","article-title":"Combining the theoretical bound and deep adversarial network for machinery openset diagnosis transfer","volume":"548","author":"Deng","year":"2023","journal-title":"Neurocomputing"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kang, M., Zhu, J., Zhang, R., Park, J., Shechtman, E., Paris, S., and Park, T. (2023, January 18\u201322). Scaling up gans for text-to-image synthesis. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00976"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1109\/TIFS.2023.3246766","article-title":"APMSA: Adversarial perturbation against model stealing attacks","volume":"18","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Gao, J., Zhang, J., Liu, X., Darrell, T., Shelhamer, E., and Wang, D. (2023, January 18\u201322). Back to the source: Diffusion-driven test-time adaptation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01134"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Nguyen, T.H., Van Le, T., and Tran, A. (2023, January 18\u201322). Efficient Scale-Invariant Generator with Column-Row Entangled Pixel Synthesis. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.02146"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lee, D., Lee, J.Y., Kim, D., Choi, J., and Kim, J. (2022, January 18\u201324). Fix the Noise: Disentangling Source Feature for Transfer Learning of StyleGAN. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52729.2023.01367"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, H., Zhang, W., Li, B., Wu, H., He, N., Huang, Y., Li, Y., Ghanem, B., and Zheng, Y. (2023, January 18\u201322). Improving GAN Training via Feature Space Shrinkage. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01556"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, T., Zhang, Y., Fan, Y., Wang, J., and Chen, Q. (2022, January 18\u201324). High-fidelity gan inversion for image attribute editing. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01109"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Huang, S., Wang, K., Liu, H., Chen, J., and Li, Y. (2023, January 18\u201322). Contrastive semi-supervised learning for underwater image restoration via re-liable bank. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01740"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xu, Y., Yin, Y., Jiang, L., Wu, Q., Zheng, C., Loy, C.C., Dai, B., and Wu, W. (2022, January 18\u201324). TransEditor: Transformer-based dual-space GAN for highly controllable facial editing. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00753"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"105382","DOI":"10.1016\/j.compbiomed.2022.105382","article-title":"Generative adversarial networks in medical image augmentation: A review","volume":"144","author":"Chen","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_35","unstructured":"Radford, A., Metz, L., and Chintala, S. (2016, January 2\u20134). Unsupervised representation learning with deep convolutional generative adversarial networks. Proceedings of the International Conference on Learning Representations, San Juan, Puerto Rico."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., and Paul Smolley, S. (2017, January 22\u201329). Least squares generative adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.304"},{"key":"ref_37","unstructured":"Brock, A., Donahue, J., and Simonyan, K. (2019, January 6\u20139). Large scale GAN training for high fidelity natural image synthesis. Proceedings of the International Conference on Learning Representations, New Orleans, LA, USA."},{"key":"ref_38","unstructured":"Chang, T.-Y., and Lu, C.-J. (December, January 30). Tinygan: Distilling biggan for conditional image generation. Proceedings of the Asian Conference on Computer Vision, Kyoto, Japan."},{"key":"ref_39","unstructured":"Karras, T., Aila, T., Laine, S., and Lehtinen, J. (2017, January 21\u201326). Progressive growing of gans for improved quality, stability, and variation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_40","unstructured":"Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A. (2019, January 10\u201315). Self-attention generative adversarial networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., and Aila, T. (2019, January 15\u201320). A style-based generator architecture for generative adversarial networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3313","DOI":"10.1109\/TKDE.2021.3130191","article-title":"A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications","volume":"35","author":"Gui","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention-CMICCAI 2015: 18th International Conference, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhu, J.-Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Schonfeld, E., Schiele, B., and Khoreva, A. (2021, January 10\u201317). A u-net based discriminator for generative adversarial networks. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/CVPR42600.2020.00823"},{"key":"ref_47","unstructured":"Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y. (May, January 30). Spectral normalization for generative adversarial networks. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wu, Y., Shuai, H., Tam, Z., and Chiu, H. (2021, January 10\u201317). Gradient normalization for generative adversarial networks. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00631"},{"key":"ref_49","unstructured":"Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X. (2016, January 5\u201310). Improved techniques for training gans. Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_50","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. (2017, January 4\u20139). Gans trained by a two time-scale update rule converge to a local nash equilibrium. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_51","unstructured":"Arjovsky, M., and Bottou, L. (2017, January 24\u201326). Towards principled methods for training generative adversarial networks. Proceedings of the International Conference on Learning Representations, Toulon, France."},{"key":"ref_52","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the 32nd International Conference on Machine Learning, Lille, France."},{"key":"ref_53","unstructured":"Kurach, K., Lucic, M., Zhai, X., Michalski, M., and Gelly, S. (2019, January 10\u201315). A large-scale study on regularization and normalization in GANs. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_54","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017, January 6\u201311). Wasserstein generative adversarial networks. Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_55","unstructured":"Thanh-Tung, H., Tran, T., and Venkatesh, S. (2019, January 6\u20139). Improving generalization and stability of generative adversarial networks. Proceedings of the International Conference on Learning Representations, New Orleans, LA, USA."},{"key":"ref_56","unstructured":"Terj\u00e9k, D. (2020, January 26\u201330). Adversarial lipschitz regularization. Proceedings of the International Conference on Learning Representations, Ababa, Ethiopia."},{"key":"ref_57","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A.C. (2017, January 4\u20139). Improved training of wasserstein gans. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_58","unstructured":"Wei, X., Gong, B., Liu, Z., Lu, W., and Wang, L. (May, January 30). Improving the improved training of wasserstein GANs. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Wu, J., Huang, Z., Thoma, J., Acharya, D., and Van Gool, L. (2018, January 8\u201314). Wasserstein divergence for gans. Proceedings of the Computer Vision\u2013ECCV 2018: 15th European Conference, Munich, Germany.","DOI":"10.1007\/978-3-030-01228-1_40"},{"key":"ref_60","unstructured":"Liu, K., Tang, W., Zhou, F., and Qiu, G. (November, January 27). Spectral regularization for combating mode collapse in gans. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_61","unstructured":"Jiang, H., Chen, Z., Chen, M., Liu, F., Wang, D., and Zhao, T. (2019, January 6\u20139). On computation and generalization of generative adversarial networks under spectrum control. Proceedings of the International Conference on Learning Representations, New Orleans, LA, USA."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., and Wang, J. (2019, January 15\u201320). Deep high-resolution representation learning for human pose estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_63","unstructured":"Coates, A., Ng, A., and Lee, H. (2011, January 11\u201313). An analysis of single-layer networks in unsupervised feature learning. Proceedings of the Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, Lauderdale, FL, USA."},{"key":"ref_64","unstructured":"Karras, T., Aila, T., Laine, S., and Lehtinen, J. (2017, January 24\u201326). Progressive growing of gans for improved quality, stability, and variation. Proceedings of the International Conference on Learning Representations, Toulon, France."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Park, M., Lee, M., and Yu, S. (2022). HRGAN: A Generative Adversarial Network Producing Higher-Resolution Images than Training Sets. Sensors, 22.","DOI":"10.3390\/s22041435"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/17\/7338\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:40:02Z","timestamp":1760128802000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/17\/7338"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,23]]},"references-count":65,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["s23177338"],"URL":"https:\/\/doi.org\/10.3390\/s23177338","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,23]]}}}