Improved Embeddings with Easy Positive Triplet Mining
WACV 2020 之前看到很多triplet selection strategy都是基於尋找negative，比如facenet中提出的semi-hard negative（不用hard negative是因爲the hardest negative examples leads to bad training behavior）==針對hard negative可以看另外一篇論文：Hard
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相關文章
- 1. 【筆記】Loc2Vec: Learning location embeddings with triplet-loss networks
- 2. Triplet Loss入門
- 3. Tensorflow實現Triplet Loss
- 4. Triplet Loss, Ranking Loss, Margin Loss
- 5. Training Region-based Object Detectors with Online Hard Example Mining
- 6. Improved Baselines with Momentum Contrastive Learning
- 7. Triplet-Loss原理及其實現、應用
- 8. Question Answering with Subgraph Embeddings筆記
- 9. Person Re-Identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function 閱讀筆記
- 10. Unsupervised Hard Example Mining from Videos for Improved Object Detection論文精讀