AutoGAN: Neural Architecture Search for Generative Adversarial Networks閱讀筆記
本文是ICCV2019的一篇文章，第一次將GAN利用網絡架構搜索(NAS)來實現，非常有啓發意義，遂寫一篇閱讀筆記。 網絡架構搜索在圖像分類等問題上均取得了一定的成功，這篇文章首次將NAS應用於GAN生成問題。我們定義了生成器結構變量的搜索空間，並使用一個RNN控制器來指導搜索，通過參數共享和動態重置來加速搜索過程。以inception得分作爲獎勵，引入多級搜索策略，以漸進的方式進行搜索。實驗驗證
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相關文章
- 1. A Style-Based Generator Architecture for Generative Adversarial Networks---閱讀筆記
- 2. BEGAN: Boundary Equilibrium Generative Adversarial Networks閱讀筆記
- 3. 論文閱讀分享（一）AutoGAN-Distiller:Searching to Compress Generative Adversarial Networks
- 4. 【閱讀筆記】Generative Adversarial Nets
- 5. AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks, ICML2020
- 6. 論文閱讀筆記《Generative Adversarial Residual Pairwise Networks for One Shot Learning 》
- 7. 【論文閱讀筆記】Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
- 8. NASE Learning Knowledge Graph Embedding for Link Prediction via Neural Architecture Search 閱讀筆記
- 9. Generative Adversarial Networks: An Overview文獻閱讀筆記
- 10. CartoonGAN: Generative Adversarial Networks for Photo Cartoonization