ECCV2016【論文筆記】Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
1、INTRO 本文作者旨在通過解決拼圖問題來進行self-supervised learning，這樣可以訓練一個網絡去識別目標的組成部分。 2、Solving Jigsaw Puzzles 當前一種解決拼圖問題的方法是將9個tiles疊在一起，每張圖又是三通道，所以就變成27通道。這種方法問題在於網絡會更傾向學習圖片間的低階信息的相關性，比如說圖片結構或者紋理邊框。 但是解決拼圖問題並不需要網
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相關文章
- 1. 自監督學習（十）Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
- 2. [解讀] Deep Clustering for Unsupervised Learning of Visual Features
- 3. 【論文筆記】Joint Unsupervised Learning of Deep Representations and Image Clusters
- 4. 【論文筆記】Unsupervised Learning of Video Representations using LSTMs
- 5. 論文筆記----DeepWalk: Online Learning of Social Representations
- 6. 論文筆記：DeepWalk: Online Learning of Social Representations
- 7. [論文筆記]Unsupervised Visual Representation Learning by Context Prediction
- 8. A Simple Framework for Contrastive Learning of Visual Representations
- 9. 論文閱讀筆記：Momentum Contrast for Unsupervised Visual Representation Learning
- 10. 【論文筆記】Unsupervised Discovery of Object Landmarks as Structural Representations