IMPROVING GENERATIVE ADVERSARIAL NETWORKS WITH DENOISING FEATURE MATCHING（Bingio-ICLR2017）
IMPROVING GENERATIVE ADVERSARIAL NETWORKS WITH DENOISING FEATURE MATCHING（Bingio-ICLR2017） 通過消噪特徵匹配改進生成式對抗網絡 摘要： 我們提出了一種針對生成對抗網絡的增強訓練程序，旨在通過將生成器引導至抽象鑑別器特徵的可能配置來解決原始缺陷。 我們使用降噪自動編碼器估算並跟蹤從數據計算出的這些特徵的分佈，並
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相關文章
- 1. 【文獻閱讀】Feature Denoising for Improving Adversarial Robustness
- 2. Enhancing Perceptual Loss with Adversarial Feature Matching for Super-Resolution
- 3. Face Aging with Contextual Generative Adversarial Nets
- 4. Finding Tiny Faces in the Wild with Generative Adversarial Network
- 5. Generative Adversarial Network (GAN) papers (2018.2.4 更新）
- 6. Generative Adversarial Nets[BEGAN]
- 7. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS
- 8. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORK
- 9. GAN學習筆記:MMGAN:Manifold-Matching Generative Adversarial Network
- 10. Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models