MV–MR: Multi-Views and Multi-Representations for Self-Supervised Learning and Knowledge Distillation

Abstract

We present a new method of self-supervised learning and knowledge distillation based on multi-views and multi-representations (MV–MR). MV–MR is based on the maximization of dependence between learnable embeddings from augmented and non-augmented views, jointly with the maximization of dependence between learnable embeddings from the augmented view and multiple non-learnable representations from the non-augmented view. We show that the proposed method can be used for efficient self-supervised classification and model-agnostic knowledge distillation. Unlike other self-supervised techniques, our approach does not use any contrastive learning, clustering, or stop gradients. MV–MR is a generic framework allowing the incorporation of constraints on the learnable embeddings via the usage of image multi-representations as regularizers. The proposed method is used for knowledge distillation. MV–MR provides state-of-the-art self-supervised performance on the STL10 and CIFAR20 datasets in a linear evaluation setup. We show that a low-complexity ResNet50 model pretrained using proposed knowledge distillation based on the CLIP ViT model achieves state-of-the-art performance on STL10 and CIFAR100 datasets.

Keywords

• image representation learning
• self-supervised learning
• knowledge distillation
• semi-supervised learning