Model-Aware Contrastive Learning: Towards Escaping Uniformity-Tolerance Dilemma in Training

Instance discrimination contrastive learning (CL) has achieved significant success in learning transferable representations. A hardness-aware property related to the temperature $ \tau $ of the CL loss is identified to play an essential role in automatically concentrating on hard negative samples. However, previous work also proves that there exists a uniformity-tolerance dilemma (UTD) in CL loss, which will lead to unexpected performance degradation. Specifically, a smaller temperature helps to learn separable embeddings but has less tolerance to semantically related samples, which may result in suboptimal embedding space, and vice versa. In this paper, we propose a Model-Aware Contrastive Learning (MACL) strategy to escape UTD. For the undertrained phases, there is less possibility that the high similarity region of the anchor contains latent positive samples. Thus, adopting a small temperature in these stages can impose larger penalty strength on hard negative samples to improve the discrimination of the CL model. In contrast, a larger temperature in the well-trained phases helps to explore semantic structures due to more tolerance to potential positive samples. During implementation, the temperature in MACL is designed to be adaptive to the alignment property that reflects the confidence of a CL model. Furthermore, we reexamine why contrastive learning requires a large number of negative samples in a unified gradient reduction perspective. Based on MACL and these analyses, a new CL loss is proposed in this work to improve the learned representations and training with small batch size.