Adaptive Proxy Anchor Loss for Deep Metric Learning

Abstract

Deep metric learning (or simply called metric learning) uses the deep neural network to learn the representation of images, leading to widely used in many applications, e.g. image retrieval and face recognition. In the metric learning approaches, proxy anchor takes advantage of proxy-based and pair-based approaches to enable fast convergence time and robustness to noisy labels. However, in training the proxy anchor, selecting the hyperparameter margin is important to achieve a good performance. This selection requires expertise and is time-consuming. This paper proposes a novel method to learn the margin while training the proxy anchor approach adaptively. The proposed adaptive proxy anchor simplifies the hyperparameter tuning process while advancing the proxy anchor. We achieve state of the art on three public datasets with a noticeably faster convergence time. Our code is available at https:

Margin and Shared Proxies: Advanced Proxy Anchor Loss for Out-of-Domain Intent Classification

“…The concept of the margin also exists in Proxy-Anchor loss. However, since the margin of the proxy-anchor loss is a hyperparameter, the researchers set the margin, and there is a limitation in that several experiments must be conducted to find the optimal value [17]. Moreover, there have been various studies aiming to learn the margin that determines the decision boundary in the OOD Detection task [10,34,35], but since the low-dimensional distance itself is set as a learning parameter, these approaches have been limited in terms of their ability to capture the features of the high-dimensional embedding vector.…”

“…In previous studies [10,17], the decision boundary has been created by directly calculating the scalar distance from the center of the IND intents when learning the decision boundary. However, since these methods only learn whether each sample is located inside the decision boundary, it is difficult for them to capture high-dimensional features.…”