JavaShuo
欄目
標籤
Y-Autoencoders: disentangling latent representations via sequential-encoding
時間 2021-01-13
原文
原文鏈接
論文鏈接:https://arxiv.org/pdf/1907.10949.pdf 代碼鏈接:https://github.com/mpatacchiola/Y-AE 前言 Y-Autoencoders是2019年CVPR上的一篇的論文,這篇論文的創新點在於之前的Autoencoders的輸入和輸出一致,所以其主要用於圖像壓縮方面,對於Autoencoders的架構不清楚的可以參考我這篇博客,但是
>>阅读原文<<
相關文章
1.
[ICCV2019] Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis
2.
[MICCAI2019]Unsupervised Domain Adaptation via Disentangled Representations
3.
[TMI2018-03]Multimodal MR Synthesis via Modality-Invariant Latent Representation
4.
Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver
5.
Taskonomy: Disentangling Task Transfer Learning
6.
【NLP】latent Dirichlet allocation
7.
論文筆記《Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation》
8.
[論文筆記]Unsupervised Domain-Specific Deblurring via Disentangled Representations(CVPR2019)
9.
論文筆記- Improving Word Representations via Global Context and Multiple Word Prototypes
10.
[MICCAI2019] Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-M
更多相關文章...
•
PHP is_uploaded_file() 函數
-
PHP參考手冊
相關標籤/搜索
representations
latent
CLR via C#
0
分享到微博
分享到微信
分享到QQ
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。
最新文章
1.
1.2 Illustrator多文檔的幾種排列方式
2.
5.16--java數據類型轉換及雜記
3.
性能指標
4.
(1.2)工廠模式之工廠方法模式
5.
Java記錄 -42- Java Collection
6.
Java記錄 -42- Java Collection
7.
github使用
8.
Android學習筆記(五十):聲明、請求和檢查許可
9.
20180626
10.
服務擴容可能引入的負面問題及解決方法
本站公眾號
歡迎關注本站公眾號,獲取更多信息
相關文章
1.
[ICCV2019] Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis
2.
[MICCAI2019]Unsupervised Domain Adaptation via Disentangled Representations
3.
[TMI2018-03]Multimodal MR Synthesis via Modality-Invariant Latent Representation
4.
Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver
5.
Taskonomy: Disentangling Task Transfer Learning
6.
【NLP】latent Dirichlet allocation
7.
論文筆記《Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation》
8.
[論文筆記]Unsupervised Domain-Specific Deblurring via Disentangled Representations(CVPR2019)
9.
論文筆記- Improving Word Representations via Global Context and Multiple Word Prototypes
10.
[MICCAI2019] Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-M
>>更多相關文章<<