Learning-aware feature denoising discriminator

I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial nets, in: Proceedings of International Conference on Neural Information Processing Systems (NeurIPS), 2014, pp. 2672–2680. Shamsolmoali, 2021, Image synthesis with adversarial networks: a comprehensive survey and case studies, Inf. Fusion, 72, 126, 10.1016/j.inffus.2021.02.014 Mirza, 2014 M. Arjovsky, S. Chintala, L. Bottou, Wasserstein generative adversarial networks, in: Proceedings of International Conference on Machine Learning (ICML), 2017, pp. 214–223. X. Mao, Q. Li, H. Xie, R.Y. Lau, Z. Wang, S. Paul Smolley, Least squares generative adversarial networks, in: Proceedings of IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2794–2802. C. Shen, Y. Yin, X. Wang, X. Li, J. Song, M. Song, Training generative adversarial networks in one stage, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 3350–3360. J.-Y. Zhu, T. Park, P. Isola, A.A. Efros, Unpaired image-to-image translation using cycle-consistent adversarial networks, in: Proceedings of IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2223–2232. Lee, 2020, DRIT++: Diverse image-to-image translation via disentangled representations, Int. J. Comput. Vis., 1 Z. Shen, M. Huang, J. Shi, X. Xue, T.S. Huang, Towards instance-level image-to-image translation, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 3683–3692. Wang, 2021, DiCyc: GAN-based deformation invariant cross-domain information fusion for medical image synthesis, Inf. Fusion, 67, 147, 10.1016/j.inffus.2020.10.015 S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, H. Lee, Generative adversarial text to image synthesis, in: Proceedings of International Conference on Machine Learning (ICML), 2016, pp. 1060–1069. Gao, 2021, Lightweight dynamic conditional GAN with pyramid attention for text-to-image synthesis, Pattern Recognit., 110, 10.1016/j.patcog.2020.107384 Zhang, 2018, STACKGAN++: Realistic image synthesis with stacked generative adversarial networks, IEEE Trans. Pattern Anal. Mach. Intell., 41, 1947, 10.1109/TPAMI.2018.2856256 S. Nam, Y. Kim, S.J. Kim, Text-adaptive generative adversarial networks: Manipulating images with natural language, in: Proceedings of International Conference on Neural Information Processing Systems (NeurIPS), 2018, pp. 42–51. Yuan, 2019, Bridge-GAN: Interpretable representation learning for text-to-image synthesis, IEEE Trans. Circuits Syst. Video Technol., 30, 4258, 10.1109/TCSVT.2019.2953753 Zheng, 2020, DMGAN: Adversarial learning-based decision making for human-level plant-wide operation of process industries under uncertainties, IEEE Trans. Neural Netw. Learn. Syst., 32, 985, 10.1109/TNNLS.2020.2979800 X. Qian, Y. Fu, T. Xiang, W. Wang, J. Qiu, Y. Wu, Y.-G. Jiang, X. Xue, Pose-normalized image generation for person re-identification, in: Proceedings of European Conference on Computer Vision (ECCV), 2018, pp. 650–667. Y. Xian, T. Lorenz, B. Schiele, Z. Akata, Feature generating networks for zero-shot learning, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 5542–5551. Kang, 2020, Enhanced subspace distribution matching for fast visual domain adaptation, IEEE Trans. Comput. Soc. Syst., 7, 1047, 10.1109/TCSS.2020.3001517 M. Arjovsky, L. Bottou, Towards principled methods for training generative adversarial networks, in: Proceedings of International Conference on Learning Representations (ICLR), 2017. X.B. Peng, A. Kanazawa, S. Toyer, P. Abbeel, S. Levine, Variational discriminator bottleneck: Improving imitation learning, inverse RL, and GANs by constraining information flow, in: Proceedings of International Conference on Learning Representations (ICLR), 2019. Radford, 2015 T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, X. Chen, Improved techniques for training GANs, in: Proceedings of International Conference on Neural Information Processing Systems (NeurIPS), 2016, pp. 2234–2242. T. Miyato, T. Kataoka, M. Koyama, Y. Yoshida, Spectral normalization for generative adversarial networks, in: Proceedings of International Conference on Learning Representations (ICLR), 2018. T.-C. Wang, M.-Y. Liu, J.-Y. Zhu, A. Tao, J. Kautz, B. Catanzaro, High-resolution image synthesis and semantic manipulation with conditional GANs, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 8798–8807. Gan, 2018, GANs with multiple constraints for image translation, Complexity, 2018, 10.1155/2018/4613935 P. Vincent, H. Larochelle, Y. Bengio, P.-A. Manzagol, Extracting and composing robust features with denoising autoencoders, in: Proceedings of International Conference on Machine Learning (ICML), 2008, pp. 1096–1103. A. Odena, C. Olah, J. Shlens, Conditional image synthesis with auxiliary classifier GANs, in: Proceedings of International Conference on Machine Learning (ICML), 2017, pp. 2642–2651. Zhang, 2019, Image de-raining using a conditional generative adversarial network, IEEE Trans. Circuits Syst. Video Technol., 30, 3943, 10.1109/TCSVT.2019.2920407 J. Bao, D. Chen, F. Wen, H. Li, G. Hua, CVAE-GAN: fine-grained image generation through asymmetric training, in: Proceedings of IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2745–2754. Z. Zheng, L. Sun, Disentangling latent space for vae by label relevant/irrelevant dimensions, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 12192–12201. S. Nowozin, B. Cseke, R. Tomioka, F-GAN: Training generative neural samplers using variational divergence minimization, in: Proceedings of International Conference on Neural Information Processing Systems (NeurIPS), 2016, pp. 271–279. Nguyen, 2010, Estimating divergence functionals and the likelihood ratio by convex risk minimization, IEEE Trans. Inf. Theory, 56, 5847, 10.1109/TIT.2010.2068870 X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, P. Abbeel, INFOGAN: Interpretable representation learning by information maximizing generative adversarial nets, in: Proceedings of International Conference on Neural Information Processing Systems (NeurIPS), 2016, pp. 2172–2180. Wu, 2007, Robust truncated hinge loss support vector machines, J. Amer. Statist. Assoc., 102, 974, 10.1198/016214507000000617 Lim, 2017 Kim, 2021, Local stability of wasserstein GANs with abstract gradient penalty, IEEE Trans. Neural Netw. Learn. Syst., 1 Wang, 2020, Stabilizing training of generative adversarial nets via langevin stein variational gradient descent, IEEE Trans. Neural Netw. Learn. Syst., 1 M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, S. Hochreiter, GANs trained by a two time-scale update rule converge to a local nash equilibrium, in: Proceedings of International Conference on Neural Information Processing Systems (NeurIPS), 2017, pp. 6626–6637. H. Zhang, Z. Zhang, A. Odena, H. Lee, Consistency regularization for generative adversarial networks, in: Proceedings of International Conference on Learning Representations (ICLR), 2020. A. Karnewar, O. Wang, MSG-GAN: Multi-scale gradients for generative adversarial networks, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 7799–7808. H. Zhang, I. Goodfellow, D. Metaxas, A. Odena, Self-attention generative adversarial networks, in: Proceedings of International Conference on Machine Learning (ICML), 2019, pp. 7354–7363. Brock, 2018 P. Isola, J.-Y. Zhu, T. Zhou, A.A. Efros, Image-to-image translation with conditional adversarial networks, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1125–1134. J.-Y. Zhu, R. Zhang, D. Pathak, T. Darrell, A.A. Efros, O. Wang, E. Shechtman, Toward multimodal image-to-image translation, in: Proceedings of International Conference on Neural Information Processing Systems (NeurIPS), 2017, pp. 465–476. H.-Y. Lee, H.-Y. Tseng, J.-B. Huang, M. Singh, M.-H. Yang, Diverse image-to-image translation via disentangled representations, in: Proceedings of European Conference on Computer Vision (ECCV), 2018, pp. 35–51. X. Huang, M.-Y. Liu, S. Belongie, J. Kautz, Multimodal unsupervised image-to-image translation, in: Proceedings of European Conference on Computer Vision (ECCV), 2018, pp. 172–189. Huang, 2020 Y. Choi, Y. Uh, J. Yoo, J.-W. Ha, STARGAN V2: Diverse image synthesis for multiple domains, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 8188–8197. Xia, 2020, Unsupervised multi-domain multimodal image-to-image translation with explicit domain-constrained disentanglement, Neural Netw., 131, 50, 10.1016/j.neunet.2020.07.023 Y.-C. Chen, X. Xu, Z. Tian, J. Jia, Homomorphic latent space interpolation for unpaired image-to-image translation, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 2408–2416. Tao, 2020 Li, 2020, Exploring global and local linguistic rRepresentation for text-to-image synthesis, IEEE Trans. Multimed., 22, 3075, 10.1109/TMM.2020.2972856 Wang, 2020, End-to-end text-to-image synthesis with spatial constrains, ACM Trans. Intell. Syst. Technol. (TIST), 11, 1 H. Dong, S. Yu, C. Wu, Y. Guo, Semantic image synthesis via adversarial learning, in: Proceedings of IEEE International Conference on Computer Vision (ICCV), 2017, pp. 5706–5714. Gan, 2020, Sentence guided object color change by adversarial learning, Neurocomputing, 377, 113, 10.1016/j.neucom.2019.10.012 Zhou, 2020, Deep learning fault diagnosis method based on global optimization GAN for unbalanced data, Knowl.-Based Syst., 187, 10.1016/j.knosys.2019.07.008 Z. Liu, P. Luo, X. Wang, X. Tang, Deep learning face attributes in the wild, in: Proceedings of IEEE International Conference on Computer Vision (ICCV), 2015, pp. 3730–3738. T. Karras, T. Aila, S. Laine, J. Lehtinen, Progressive growing of GANs for improved quality, stability, and variation, in: Proceedings of International Conference on Learning Representations (ICLR), 2018. Yu, 2015 Gan, 2018, Unpaired cross domain image translation with augmented auxiliary domain information, Neurocomputing, 316, 112, 10.1016/j.neucom.2018.07.057 M.-E. Nilsback, A. Zisserman, Automated flower classification over a large number of classes, in: Proceedings of Indian Conference on Computer Vision, Graphics & Image Processing (ICVGIP), 2008, pp. 722–729. I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, A. Courville, Improved training of Wasserstein GANs, in: Proceedings of International Conference on Neural Information Processing Systems (NeurIPS), 2017, pp. 5769–5779. Gan, 2019, Generative adversarial networks with denoising penalty and sample augmentation, Neural Comput. Appl., 1 D.P. Kingma, J. Ba, Adam: A method for stochastic optimization, in: Proceedings of International Conference on Learning Representations (ICLR), 2015. Abdi, 2010, Coefficient of variation, 169