Adversarial Training Reconstruction读后感
本文思路是根据合成图像的体素重建来推理真实图像的体素重建，并利用了对抗训练。整个框架如下： 可以看到有三个encoder和decoder。分别细说。 混淆图像域： encoder是f，decoder是D_img。D_img分类器是分类合成图像和真实图像，为了实现域混淆，希望合成图像的域和真实图像的域不那么明显，也就是说分类器不能很好分类这两种图像，所以D_img分类器的参数要最大化这个分类误差，而
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相关文章
- 1. CS231n Lecture 16 | Adversarial Examples and Adversarial Training
- 2. Domain-Adversarial Training of Neural Networks
- 3. Directional Adversarial Training for Recommender Systems
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- 5. Boosting Adversarial Training with Hypersphere Embedding
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- 9. Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction
- 10. Adversarial Autoencoder