Adaptive Proxy Anchor Loss for Deep Metric Learning
Date
2022-10Author
Nguyen, Do Trung Chanh
Nguyen, Phan
Tran, Sen
Ta, Duc Huy
Duong, T.M. Soan
Bui, Trung
Truong, Q.H. Steven
Pham, Hong Thinh
Nguyen, Thanh Dat
Nguyen, Huu Thanh
Nguyen, M. Hien
Truong, Thu Huong
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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://github.com/tks1998/Adaptive-Proxy-Anchor.
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