Deep Metric Learning with Hierarchical Triplet Loss 阅读笔记
创新点和动机 文章提出了 hierarchical triplet loss (HTL) 方法，该方法主要通过定义一个编码上下文信息的树来收集信息样本，改进了在 mini-batch 中随机取样导致的陷入局部最优和收敛速度慢等问题。 解决方法 给定使用传统 triplet loss 预训练的神经网络 ϕ t ( ⋅ , θ ) ( ∈ R d ) \phi_{t}(\cdot, \boldsymb
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相关文章
- 1. 【2017_ICCV】Deep Metric Learning with Angular Loss
- 2. 【笔记】Loc2Vec: Learning location embeddings with triplet-loss networks
- 3. 阅读笔记 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 阅读笔记
- 7. 论文阅读理解 - Deep Metric Learning via Lifted Structured Feature Embedding
- 8. Vehicle Re-identification with Viewpoint-aware Metric Learning 读书笔记
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- 10. Machine Learning & Deep Learning 论文阅读笔记