Show, Attend and Translate: Unsupervised Image Translation with Self-Regularization and Attention 解讀
本文提出了一種結合Attention機制和self-Regularization的無監督圖像域轉換模型。 問題 爲了解決兩個域間的圖像轉換問題，需要學習從一個域到另一個域的映射，將X域中的圖像轉換到Y域。 本文的目的是使生成的圖像看起來和源圖像(X域)是相似的，同時具備Y域圖像的特徵。 如上圖所示，圖像從Horse域轉換到了Zebra域，但是背景部分並沒有變化。 方法 由於現在的圖像轉換方法經常會
相關文章
- 1. 《Show, Attend and Tell: Neural Image Caption Generation with Visual Attention》閱讀筆記
- 2. Unsupervised Attention-guided Image-to-Image Translation 解讀
- 3. [Paper Reading] Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
- 4. [Image Caption系列(1)] Show attend and tell論文解讀
- 5. image caption解讀系列（二）：《Show, Attend and Tell_Neural Image Caption》
- 6. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention論文閱讀筆記
- 7. attention(show, attention and tell: neural image caption generation with visual attention)
- 8. 零基礎學nlp【5】 hard attention 和 soft attention（Show, attend and tell: Neural image caption generation ）
- 9. attention model--Neural machine translation by jointly learning to align and translate論文解讀
- 10. Translation with a Sequence to Sequence Network and Attention
- 更多相關文章...
- • XSL-FO table-and-caption 對象 - XSL-FO 教程
- • W3C RDF and OWL 活動 - W3C 教程
- • RxJava操作符（七）Conditional and Boolean
- • JDK13 GA發佈：5大特性解讀
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. 《Show, Attend and Tell: Neural Image Caption Generation with Visual Attention》閱讀筆記
- 2. Unsupervised Attention-guided Image-to-Image Translation 解讀
- 3. [Paper Reading] Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
- 4. [Image Caption系列(1)] Show attend and tell論文解讀
- 5. image caption解讀系列（二）：《Show, Attend and Tell_Neural Image Caption》
- 6. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention論文閱讀筆記
- 7. attention(show, attention and tell: neural image caption generation with visual attention)
- 8. 零基礎學nlp【5】 hard attention 和 soft attention（Show, attend and tell: Neural image caption generation ）
- 9. attention model--Neural machine translation by jointly learning to align and translate論文解讀
- 10. Translation with a Sequence to Sequence Network and Attention