Research map / Label-efficient and robust learning / Self-supervised and contrastive
Learning Representations by Maximizing Mutual Information Across Views
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| maximize mutual information across augmented views with multiscale features | self-supervised encoder · ImageNet | linear eval accuracy: 68.1 | Helped |
| MLP instead of linear evaluation head | AMDIM · ImageNet | accuracy: 68.1 -> 69.5 | Helped |
| add mixture-based representations with bag-of-features evaluation (ablation) | AMDIM · STL10 | accuracy | Helped |
Closest papers by meaning
- Self-supervised Learning from a Multi-view Perspective (ICLR 2021 · similarity 0.90)
- Revisiting Self-Supervised Visual Representation Learning (CVPR 2019 · similarity 0.89)
- When Does Contrastive Visual Representation Learning Work? (CVPR 2022 · similarity 0.88)
- What Makes for Good Views for Contrastive Learning? (NeurIPS 2020 · similarity 0.88)
- Learning Representations by Predicting Bags of Visual Words (CVPR 2020 · similarity 0.88)
- Can Generative Models Improve Self-Supervised Representation Learning? (AAAI 2025 · similarity 0.87)
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