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Related Experiment Video

Updated: Jun 23, 2025

Cross-Modal Multivariate Pattern Analysis
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MV-MR: Multi-Views and Multi-Representations for Self-Supervised Learning and Knowledge Distillation.

Vitaliy Kinakh1, Mariia Drozdova1, Slava Voloshynovskiy1

  • 1Department of Computer Science, University of Geneva, 1227 Carouge, Switzerland.

Entropy (Basel, Switzerland)
|June 26, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel self-supervised learning and knowledge distillation method called multi-views and multi-representations (MV-MR). MV-MR achieves state-of-the-art performance on image classification tasks without using contrastive learning.

Keywords:
image representation learningknowledge distillationself-supervised learningsemi-supervised learning

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Self-supervised learning (SSL) is crucial for leveraging unlabeled data in machine learning.
  • Knowledge distillation aims to transfer knowledge from a larger model to a smaller one.
  • Existing SSL methods often rely on contrastive learning, clustering, or stop gradients, which can be limiting.

Purpose of the Study:

  • To introduce a new self-supervised learning and knowledge distillation framework named multi-views and multi-representations (MV-MR).
  • To demonstrate the efficacy of MV-MR for efficient self-supervised classification and model-agnostic knowledge distillation.
  • To showcase MV-MR's ability to incorporate constraints on learnable embeddings using image multi-representations as regularizers.

Main Methods:

  • The MV-MR method maximizes dependence between learnable embeddings from augmented and non-augmented views.
  • It also maximizes dependence between learnable embeddings from augmented views and non-learnable representations from non-augmented views.
  • The framework avoids contrastive learning, clustering, and stop gradients, offering a generic approach.

Main Results:

  • MV-MR achieves state-of-the-art self-supervised performance on STL10 and CIFAR20 datasets in a linear evaluation setup.
  • A ResNet50 model, pretrained using MV-MR knowledge distillation with a CLIP ViT model, attains state-of-the-art results on STL10 and CIFAR100.
  • The method proves effective for efficient self-supervised classification and model-agnostic knowledge distillation.

Conclusions:

  • The MV-MR framework offers a novel and effective approach to self-supervised learning and knowledge distillation.
  • It achieves superior performance compared to existing methods, particularly in linear evaluation settings.
  • MV-MR provides a flexible and powerful tool for representation learning and model compression.