APF-GAN: Exploring asymmetric pre-training and fine-tuning strategy for conditional generative adversarial network

Springer Science and Business Media LLC - Tập 10 - Trang 187-192 - 2023
Yuxuan Li1, Lingfeng Yang2, Xiang Li1
1College of Computer Science, Nankai University, Tianjin, China
2School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

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Park, T.; Liu, M. Y.; Wang, T. C.; Zhu, J. Y. Semantic image synthesis with spatially-adaptive normalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2332–2341, 2019. Isola, P.; Zhu, J. Y.; Zhou, T. H.; Efros, A. A. Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5967–5976, 2017. Mirza, M.; Osindero, S. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784, 2014. Xue, Y.; Guo, Y. C.; Zhang, H.; Xu, T.; Zhang, S. H.; Huang, X. L. Deep image synthesis from intuitive user input: A review and perspectives. Computational Visual Media Vol. 8, No. 1, 3–31, 2022. Karras, T.; Aila, T. M.; Laine, S.; Lehtinen, J. Progressive growing of GANs for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196, 2017. Richardson, E.; Alaluf, Y.; Patashnik, O.; Nitzan, Y.; Azar, Y.; Shapiro, S.; Cohen-Or, D. Encoding in style: A StyleGAN encoder for image-to-image translation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2287–2296, 2021. Zhou, W. Y.; Yang, G. W.; Hu, S. M. Jittor-GAN: A fast-training generative adversarial network model zoo based on Jittor. Computational Visual Media Vol. 7, No. 1, 153–157, 2021. Hu, S. M.; Liang, D.; Yang, G. Y.; Yang, G. W.; Zhou, W. Y. Jittor: A novel deep learning framework with meta-operators and unified graph execution. Science China Information Sciences Vol. 63, No. 12, 222103, 2020. Xie, E. Z.; Wang, W. H.; Yu, Z. D.; Anandkumar, A.; Alvarez, J.; Luo, P. SegFormer: Simple and efficient design for semantic segmentation with transformers. In: Proceedings of the 35th Conference on Neural Information Processing Systems, 12077–12090, 2021. She, D. Y.; Lai, Y. K.; Yi, G. X.; Xu, K. Hierarchical layout-aware graph convolutional network for unified aesthetics assessment. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8471–8480, 2021. Talebi, H.; Milanfar, P. NIMA: Neural image assessment. IEEE Transactions on Image Processing Vol. 27, No. 8, 3998–4011, 2018. Lee, J. T.; Kim, C. S. Image aesthetic assessment based on pairwise comparison—A unified approach to score regression, binary classification, and personalization. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, 1191–1200, 2019. Heusel, M.; Ramsauer, H.; Unterthiner, T.; Nessler, B.; Hochreiter, S. GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, 6629–6640, 2017.