Adversarial Training Reconstruction讀後感
本文思路是根據合成圖像的體素重建來推理真實圖像的體素重建，並利用了對抗訓練。整個框架如下： 可以看到有三個encoder和decoder。分別細說。 混淆圖像域： encoder是f，decoder是D_img。D_img分類器是分類合成圖像和真實圖像，爲了實現域混淆，希望合成圖像的域和真實圖像的域不那麼明顯，也就是說分類器不能很好分類這兩種圖像，所以D_img分類器的參數要最大化這個分類誤差，而
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相關文章
- 1. CS231n Lecture 16 | Adversarial Examples and Adversarial Training
- 2. Domain-Adversarial Training of Neural Networks
- 3. Directional Adversarial Training for Recommender Systems
- 4. DANN：Domain-Adversarial Training of Neural Networks
- 5. Boosting Adversarial Training with Hypersphere Embedding
- 6. 《Adversarial Training for Relation Extraction》淺析
- 7. BAG OF TRICKS FOR ADVERSARIAL TRAINING
- 8. [EMNLP2017]Adversarial Training for Relation Extraction
- 9. Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction
- 10. Adversarial Autoencoder