Learning from Simulated and Unsupervised Images through Adversarial Training：解析simGAN
通過對抗式訓練從模擬無監督圖像中學習 該篇論文爲蘋果首篇AI論文，且爲2017年cvpr的最佳論文。該論文主要通過訓練一個對抗式網絡，將合成圖像中添加真實性的細節，最終得到一個有標籤的，類似與真實圖像的合成圖像。本文將從以下幾個方面介紹該論文。 傳統GAN網絡結構 訓練過程 simGAN網絡結構 論文創新點 未來研究方向 總結 0.傳統GAN網絡 對於GAN網絡來說，最重要的兩個部分就是生成器和分
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相關文章
- 1. 【paper 2】Learning from Simulated and Unsupervised Images through Adversarial Training
- 2. 論文筆記（三） 【Learning from Simulated and Unsupervised Images through Adversarial Training】
- 3. 對抗學習之Learning from Simulated and Unsupervised Images through Adversarial Training
- 4. GAN系列：論文閱讀——SimGAN( Simulated + Unsupervised Learning )
- 5. Beyond Narrative Description: Generating Poetry from Images by Multi-Adversarial Training
- 6. Supervised learning and Unsupervised learning
- 7. CS231n Lecture 16 | Adversarial Examples and Adversarial Training
- 8. Machine learning and Classifier from Wiki
- 9. [論文翻譯]Intramodality Domain Adaptation Using Self Ensembling and Adversarial Training
- 10. 論文筆記：Learning task-oriented grasping for tool manipulation from simulated self-supervision