mlsanityData to 7 Oct 2026

Unsupervised Visual Representation Learning via Mutual Information Regularized Assignment

arXiv 2211.02284 ↗First posted 4 Nov 2022cs.CVcited by 7

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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.

References · 40

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Citing papers per year: 2023: 3; 2024: 3; 2025: 1.

7 papers.