Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition
This work addresses the need for improved feature learning in online knowledge distillation for visual recognition, offering an incremental enhancement over existing approaches.
The paper tackles the problem of teacher-free online knowledge distillation by introducing a Mutual Contrastive Learning (MCL) framework that leverages feature representational information through mutual contrastive distributions among student networks, resulting in consistent performance gains in image classification and transfer learning tasks compared to state-of-the-art methods.
The teacher-free online Knowledge Distillation (KD) aims to train an ensemble of multiple student models collaboratively and distill knowledge from each other. Although existing online KD methods achieve desirable performance, they often focus on class probabilities as the core knowledge type, ignoring the valuable feature representational information. We present a Mutual Contrastive Learning (MCL) framework for online KD. The core idea of MCL is to perform mutual interaction and transfer of contrastive distributions among a cohort of networks in an online manner. Our MCL can aggregate cross-network embedding information and maximize the lower bound to the mutual information between two networks. This enables each network to learn extra contrastive knowledge from others, leading to better feature representations, thus improving the performance of visual recognition tasks. Beyond the final layer, we extend MCL to intermediate layers and perform an adaptive layer-matching mechanism trained by meta-optimization. Experiments on image classification and transfer learning to visual recognition tasks show that layer-wise MCL can lead to consistent performance gains against state-of-the-art online KD approaches. The superiority demonstrates that layer-wise MCL can guide the network to generate better feature representations. Our code is publicly avaliable at https://github.com/winycg/L-MCL.