Unsupervised Visual Representation Learning via Mutual Information Regularized Assignment
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Abstract (arXiv)
This paper proposes Mutual Information Regularized Assignment (MIRA), a pseudo-labeling algorithm for unsupervised representation learning inspired by information maximization. We formulate online pseudo-labeling as an optimization problem to find pseudo-labels that maximize the mutual information between the label and data while being close to a given model probability. We derive a fixed-point iteration method and prove its convergence to the optimal solution. In contrast to baselines, MIRA combined with pseudo-label prediction enables a simple yet effective clustering-based representation learning without incorporating extra training techniques or artificial constraints such as sampling strategy, equipartition constraints, etc. With relatively small training epochs, representation learned by MIRA achieves state-of-the-art performance on various downstream tasks, including the linear/k-NN evaluation and transfer learning. Especially, with only 400 epochs, our method applied to ImageNet dataset with ResNet-50 architecture achieves 75.6% linear evaluation accuracy.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 40 of its 61 references that are in the map. First posted on arXiv 4 Nov 2022.
Builds on
40 of 61 references.
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Built on by
Citing papers per year: 2023: 3; 2024: 3; 2025: 1.
7 papers.
- 2024HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysiscited by 104
- 2023A General-Purpose Self-Supervised Model for Computational Pathologycited by 42
- 2023SIESTA: Efficient Online Continual Learning with Sleepcited by 24
- 2023Representation Learning via Consistent Assignment of Views over Random Partitionscited by 2
- 2024Efficient Unsupervised Visual Representation Learning with Explicit Cluster Balancingcited by 1
- 2024Mutual Information Guided Optimal Transport for Unsupervised Visible-Infrared Person Re-identificationcited by 1
- 2025A Tutorial on Discriminative Clustering and Mutual Informationcited by 0
- 2025A Tutorial on Discriminative Clustering and Mutual Information7 May 2025
- 2024Mutual Information Guided Optimal Transport for Unsupervised Visible-Infrared Person Re-identification17 Jul 2024
- 2024Efficient Unsupervised Visual Representation Learning with Explicit Cluster Balancing15 Jul 2024
- 2024HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis23 Jun 2024
- 2023Representation Learning via Consistent Assignment of Views over Random Partitions19 Oct 2023
- 2023A General-Purpose Self-Supervised Model for Computational Pathology29 Aug 2023
- 2023SIESTA: Efficient Online Continual Learning with Sleep19 Mar 2023