In this paper we extend the proxy-anchor method by posing it within the continual learning framework, motivated from its batch-expected loss form (instead of ...
The recent proxy-anchor method achieved out- standing performance in deep metric learning, which can be acknowledged to its data efficient.
Variational Continual Proxy-Anchor for Deep Metric Learning
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A framework to enhance generalization of deep metric learning methods using general discriminative feature learning and class adversarial neural networks.
In this paper we extend the proxy-anchor method by posing it within the continual learning framework, motivated from its batch-expected loss form (instead of ...
Variational Continual Proxy-Anchor for Deep Metric Learning. Minyoung Kim ... The recent proxy-anchor method achieved outstanding performance in deep metric ...
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This property enables Proxy-Anchor loss to pro- vide richer supervisory signals to embedding networks dur- ing training. The gradients of the two losses ...
Missing: Variational Continual
Variational Continual Proxy-Anchor for Deep Metric Learning. 04:58. Variational Continual Proxy-Anchor for Deep Metric Learning. Watch later. Favorite. Share ...
This repository provides source code of experiments on four datasets (CUB-200-2011, Cars-196, Stanford Online Products and In-shop) and pretrained models.
Missing: Variational Continual
Jan 1, 2024 · The motivation is to set image samples as anchors to compare with proxies of different classes instead of corresponding samples to reduce ...