Unsupervised Attention-guided Image-to-Image Translation 解讀
本文和上一篇show,Attend一樣，都是基於attention機制的圖像轉換。兩篇文章的工作大體是相似的，但是這篇文章的代碼開源了。 這篇文章和上篇文章一樣都是做圖像域轉換，並且在轉換時都想要只關注圖像中的前景，達到前景轉換而背景不變的效果。 在上一篇blog中已經介紹過了圖像域轉換，所以這裏就直接開始講作者提出的模型了。 Model Fs->t表示從S域到T域的轉換。 作者定義了兩個Atte
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相關文章
- 1. Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation
- 2. Multimodal Unsupervised Image-to-Image Translation
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- 4. Show, Attend and Translate: Unsupervised Image Translation with Self-Regularization and Attention 解讀
- 5. Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation
- 6. 論文Multimodal Unsupervised Image-to-Image Translation
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- 8. 論文閱讀——Unsupervised Neural Machine Translation with Indirect Supervision
- 9. 論文閱讀——Robust Unsupervised Neural Machine Translation with Adversarial Training
- 10. 文獻閱讀筆記：Phrase-Based & Neural Unsupervised Machine Translation