Deep Metric Learning by Online Soft Mining and Class-Aware Attention
Deep Metric Learning by Online Soft Mining and Class-Aware Attention 提出OSM和CAA，分別解決兩個問題： 對於anchor，以前的方法是在一個batch內找到最難的p和最容易的n，然後pull p， push n (對比損失將pull和push分開做而triplet是一起做，但都是這個過程)，剩下的p和n都沒要，這太浪費了，因
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相關文章
- 1. Attention and Memory in Deep Learning and NLP
- 2. 【2017_ICCV】Deep Metric Learning with Angular Loss
- 3. Ranked List Loss for Deep Metric Learning
- 4. SIMPLE ONLINE AND REALTIME TRACKING WITH A DEEP ASSOCIATION METRIC（論文閱讀筆記）
- 5. Machine and Deep Learning with Python
- 6. Survey and experimental study on metric learning methods
- 7. DeepSORT論文翻譯（SIMPLE ONLINE AND REALTIME TRACKING WITH A DEEP ASSOCIATION METRIC）
- 8. 論文筆記：DeepSORT : SIMPLE ONLINE AND REALTIME TRACKING WITH A DEEP ASSOCIATION METRIC
- 9. DIM（Learning deep representations by mutual information estimation and maximization）
- 10. Reinforcement learning and Deep learning