Regularizing Generative Adversarial Networks Under Limited Data
Hung-Yu Tseng, Lu Jiang, Ce Liu, Ming-Hsuan Yang, Weilong Yang
2021年份
62顶会引用
摘要
BigGAN + DA + DA + ! !" (Ours) Size of ImageNet training set BigGAN + DA BigGAN + DA + ! !" (Ours) 10% ImageNet training set 10% ImageNet FID↓ BigGAN 42.99
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引用它的顶会 Paper62
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它引用的顶会 Paper13
- Training Generative Adversarial Networks with Limited DataTero Karras, Miika Aittala, Janne Hellsten, Samuli Laine 等NeurIPS 2020 · 被引用 2,345 次
- Early-Learning Regularization Prevents Memorization of Noisy LabelsSheng Liu, Jonathan Niles-Weed, Narges Razavian, Carlos Fernandez-GrandaNeurIPS 2020 · 被引用 798 次
- Differentiable Augmentation for Data-Efficient GAN TrainingShengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu 等NeurIPS 2020 · 被引用 707 次
- Consistency Regularization for Generative Adversarial NetworksHan Zhang, Zizhao Zhang, Augustus Odena, Honglak LeeICLR 2020 · 被引用 305 次
- Beyond Synthetic Noise: Deep Learning on Controlled Noisy LabelsLu Jiang, Di Huang, Mason Liu, Weilong YangICML 2020 · 被引用 241 次
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