Center Contrastive Loss for Metric Learning

• URL: http://arxiv.org/abs/2308.00458v1
• Date: Tue, 1 Aug 2023 11:22:51 GMT
• Title: Center Contrastive Loss for Metric Learning
• Authors: Bolun Cai, Pengfei Xiong, Shangxuan Tian
• Abstract summary: We propose a novel metric learning function called Center Contrastive Loss. It maintains a class-wise center bank and compares the category centers with the query data points using a contrastive loss. The proposed loss combines the advantages of both contrastive and classification methods.
• Score: 8.433000039153407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contrastive learning is a major studied topic in metric learning. However, sampling effective contrastive pairs remains a challenge due to factors such as limited batch size, imbalanced data distribution, and the risk of overfitting. In this paper, we propose a novel metric learning function called Center Contrastive Loss, which maintains a class-wise center bank and compares the category centers with the query data points using a contrastive loss. The center bank is updated in real-time to boost model convergence without the need for well-designed sample mining. The category centers are well-optimized classification proxies to re-balance the supervisory signal of each class. Furthermore, the proposed loss combines the advantages of both contrastive and classification methods by reducing intra-class variations and enhancing inter-class differences to improve the discriminative power of embeddings. Our experimental results, as shown in Figure 1, demonstrate that a standard network (ResNet50) trained with our loss achieves state-of-the-art performance and faster convergence.

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