Cross-domain image translation with a novel style-guided diversity loss design
Tài liệu tham khảo
M.-Y. Liu, T. Breuel, J. Kautz, Unsupervised Image-to-Image Translation Networks, in: Advances in Neural Information Processing Systems, NeurIPS, Long Beach, CA, USA, 2017, pp. 700–708.
Ul Hassan, 2021, Unpaired font family synthesis using conditional generative adversarial networks, Knowl.-Based Syst., 229, 10.1016/j.knosys.2021.107304
J.-Y. Zhu, T. Park, P. Isola, A.A. Efros, Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, ICCV, Venice,Italy, 2017, pp. 2242–2251.
Yan, 2022, IPGAN: Identity-preservation generative adversarial network for unsupervised photo-to-caricature translation, Knowl.-Based Syst., 241, 10.1016/j.knosys.2022.108223
Hedjazi, 2021, Efficient texture-aware multi-GAN for image inpainting, Knowl.-Based Syst., 217, 10.1016/j.knosys.2021.106789
T. Karras, T. Aila, S. Laine, J. Lehtinen, Progressive Growing of GANs for Improved Quality, Stability, and Variation, in: Proceedings of the International Conference on Learning Representations, ICLR, Vancouver, BC, Canada, 2018, pp. 1–12.
P. Li, S. Tu, L. Xu, GAN Flexible Lmser for Super-Resolution, in: Proceedings of the ACM International Conference on Multimedia, MM, Nice, France, 2019, pp. 756–764.
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 the European Conference on Computer Vision, ECCV, Munich, Germany, 2018, pp. 36–52.
P. Isola, J.-Y. Zhu, T. Zhou, A.A. Efros, Image-To-Image Translation With Conditional Adversarial Networks, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, Honolulu, HI, USA, 2017, pp. 1125–1134.
Y. Choi, Y. Uh, J. Yoo, J. Ha, StarGAN v2: Diverse Image Synthesis for Multiple Domains, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, Seattle, WA, USA, 2020, pp. 8185–8194.
Shao, 2021, IIT-GAT: Instance-level image transformation via unsupervised generative attention networks with disentangled representations, Knowl.-Based Syst., 225, 10.1016/j.knosys.2021.107122
Xiao, 2020, Learning class-aligned and generalized domain-invariant representations for speech emotion recognition, IEEE Trans. Emerg. Top. Comput. Intell., 4, 480, 10.1109/TETCI.2020.2972926
Xiao, 2021, CS-GAN: Cross-structure generative adversarial networks for Chinese calligraphy translation, Knowl.-Based Syst., 229, 10.1016/j.knosys.2021.107334
P. Zhang, B. Zhang, D. Chen, L. Yuan, F. Wen, Cross-Domain Correspondence Learning for Exemplar-Based Image Translation, in: IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, Seattle, WA, USA, 2020, pp. 5142–5152.
T. Li, H. Zhao, S. Wang, J. Huang, Style-Guided Image-to-Image Translation for Multiple Domains, in: Proceedings of the ICMR 2021 Workshop on Multi-Modal Pre-Training for Multimedia Understanding, MMPT, Taipei, Taiwan, 2021, pp. 28–36.
J. Wu, Z. Huang, J. Thoma, D. Acharya, L. Van Gool, Wasserstein Divergence for GANs, in: Proceedings of the European Conference on Computer Vision, ECCV, Springer, Cham, 2018, pp. 673–688.
Q. Mao, H.-Y. Lee, H.-Y. Tseng, S. Ma, M.-H. Yang, Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, Long Beach, CA, USA, 2019, pp. 1429–1437.
Zhang, 2021, High-quality face image generation using particle swarm optimization-based generative adversarial networks, Future Gener. Comput. Syst., 122, 98, 10.1016/j.future.2021.03.022
X. Huang, M.-Y. Liu, S. Belongie, J. Kautz, Multimodal Unsupervised Image-to-Image Translation, in: Proceedings of the European Conference on Computer Vision, ECCV, Munich, Germany, 2018, pp. 179–196.
J.-Y. Zhu, R. Zhang, D. Pathak, T. Dar-rell, A.A. Efros, O. Wang, E. Shechtman, Toward multimodal image-to-image translation, in: Advances in Neural Information Processing Systems, NeurIPS, Long Beach, CA, USA, 2017, pp. 465–476.
Lee, 2020, DRIT++: Diverse image-to-image translation via disentangled representations, Int. J. Comput. Vis., 128, 2402, 10.1007/s11263-019-01284-z
A. Almahairi, S. Rajeswar, A. Sordoni, P. Bachman, A.C. Courville, Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data, in: Proceedings of the International Conference on Machine Learning, ICML, Stockholmsmässan, Stockholm, Sweden, 2018, pp. 195–204.
Y. Choi, M. Choi, M. Kim, J.-W. Ha, S. Kim, J. Choo, StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, Salt Lake City, UT, USA, 2018, pp. 8789–8797.
T. Kim, M. Cha, H. Kim, J. Lee, J. Kim, Learning to Discover Cross-Domain Relations with Generative Adversarial Networks, in: Proceedings of the International Conference on Machine Learning, ICML, Sydney, NSW, Australia, 2017, pp. 1857–1865.
J. Deng, N. Cummins, M. Schmitt, K. Qian, F. Ringeval, B. Schuller, Speech-Based Diagnosis of Autism Spectrum Condition by Generative Adversarial Network Representations, in: Proceedings of the International Conference on Digital Health, ICDH, London, United Kingdom, 2017, pp. 53–57.
Yang, 2021, Intrusion detection for in-vehicle network by using single GAN in connected vehicles, J. Circuits Syst. Comput., 30, 10.1142/S0218126621500079
Chen, 2021, Deep feature learning for medical image analysis with convolutional autoencoder neural network, IEEE Trans. Big Data, 7, 750, 10.1109/TBDATA.2017.2717439
J. Fan, S. Wang, P. Yang, Y. Yang, Multi-View Facial Expression Recognition based o n Multitask Learning and Generative Adversarial Network, in: Proceedings of the IEEE International Conference on Industrial Informatics, INDIN, China, 2020, pp. 573–578.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial nets, in: Advances in Neural Information Processing Systems, NeurIPS, Montréal, Canada, 2014, pp. 2672–2680.
Wang, 2021, Multi-feature fusion tracking algorithm based on generative compression network, Future Gener. Comput. Syst., 124, 206, 10.1016/j.future.2021.05.031
Wang, 2019, Adversarial de-noising of electrocardiogram, Neurocomputing, 349, 212, 10.1016/j.neucom.2019.03.083
Yang, 2019, GAN-based semi-supervised learning approach for clinical decision support in health-IoT platform, IEEE Access, 7, 8048, 10.1109/ACCESS.2018.2888816
D.P. Kingma, M. Welling, Auto-Encoding Variational Bayes, in: Proceedings of the International Conference on Learning Representations, ICLR, Banff, Canada, 2014, pp. 1–9.
Zhao, 2020, Improving multi-agent generative adversarial nets with variational latent representation, Entropy, 22, 1055, 10.3390/e22091055
Yang, 2015, Multi-class active learning by uncertainty sampling with diversity maximization, Int. J. Comput. Vis., 113, 113, 10.1007/s11263-014-0781-x
Liu, 2020, Pair-based uncertainty and diversity promoting early active learning for person re-identification, ACM Trans. Intell. Syst. Technol., 11, 21:1, 10.1145/3372121
Valanarasu, 2022, 1
Y. Jiang, H. Zhang, J. Zhang, Y. Wang, Z.L. Lin, K. Sunkavalli, S. Chen, S. Amirghodsi, S. Kong, Z. Wang, SSH: A Self-Supervised Framework for Image Harmonization, in: IEEE/CVF International Conference on Computer Vision, ICCV, Montreal, QC, Canada, October 10-17, 2021, pp. 4812–4821.
Cun, 2020, Improving the harmony of the composite image by spatial-separated attention module, IEEE Trans. Image Process., 29, 4759, 10.1109/TIP.2020.2975979
W. Cong, J. Zhang, L. Niu, L. Liu, Z. Ling, W. Li, L. Zhang, DoveNet: Deep Image Harmonization via Domain Verification, in: IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, Seattle, WA, USA, 2020, pp. 8391–8400.
T. Karras, S. Laine, T. Aila, A Style-Based Generator Architecture for Generative Adversarial Networks, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, Long Beach, CA, USA, 2019, pp. 4396–4405.
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: Advances in Neural Information Processing Systems, NeurIPS, Long Beach, CA, USA, 2017, pp. 6626–6637.
R. Zhang, P. Isola, A.A. Efros, E. Shechtman, O. Wang, The Unreasonable Effectiveness of Deep Features as a Perceptual Metric, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, Salt Lake City, UT, USA, 2018, pp. 586–595.
Krizhevsky, 2017, ImageNet classification with deep convolutional neural networks, Commun. ACM, 60, 84, 10.1145/3065386
V.H. Kamble, M.P. Dale, Machine learning approach for longitudinal face recognition of children, in: Machine Learning for Biometrics, 2022, pp. 1–27.