Deep Metric Learning with Hierarchical Triplet Loss 閱讀筆記
創新點和動機 文章提出了 hierarchical triplet loss (HTL) 方法，該方法主要通過定義一個編碼上下文信息的樹來收集信息樣本，改進了在 mini-batch 中隨機取樣導致的陷入局部最優和收斂速度慢等問題。 解決方法 給定使用傳統 triplet loss 預訓練的神經網絡 ϕ t ( ⋅ , θ ) ( ∈ R d ) \phi_{t}(\cdot, \boldsymb
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相關文章
- 1. 【2017_ICCV】Deep Metric Learning with Angular Loss
- 2. 【筆記】Loc2Vec: Learning location embeddings with triplet-loss networks
- 3. 閱讀筆記 CCL: Cross-modal Correlation Learning with Multi-grained Fusion by Hierarchical Network
- 4. Ranked List Loss for Deep Metric Learning
- 5. M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning
- 6. FeUdal Networks for Hierarchical Reinforcement Learning 閱讀筆記
- 7. 論文閱讀理解 - Deep Metric Learning via Lifted Structured Feature Embedding
- 8. Vehicle Re-identification with Viewpoint-aware Metric Learning 讀書筆記
- 9. 閱讀筆記 CCL: Cross-modal Correlation Learning with Multi-grained Fusion by Hierarchical Network 總結
- 10. Machine Learning & Deep Learning 論文閱讀筆記