Proxy Anchor Loss for Deep Metric Learning

Proxy-based losses compute class representatives (stars) for each class. Data samples (circles) are pulled towards and push away from these class representatives (data-to-proxy). Pair-based losses pull similar data samples and push different data samples (data-to-data). The solid-green lines indicate a pull force, while the red-dashed lines indicate a push force.
The pros and cons of both proxy-based and pair-based losses.
Differences between Proxy-NCA and Proxy-Anchor in handling proxies and embedding vectors during training. Each proxy is colored in black and three different colors indicate distinct classes. The associations defined by the losses are expressed by edges, and thicker edges get larger gradients.
Accuracy in Recall@1 versus training time on the Cars- 196 dataset. Note that all methods were trained with a batch size of 150 on a single Titan Xp GPU. Proxy-anchor loss achieves the highest accuracy and converges faster than the baselines in terms of both the number of epochs and the actual training time.
Comparison of training complexities
Recall@K (%) on the CUB-200–2011 and Cars-196 datasets. Superscripts denote embedding sizes and † indicates models using larger input images. Backbone networks of the models are denoted by abbreviations: G–GoogleNet, BN–Inception with batch normalization, R50–ResNet50.

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