Papers › Compressive Visual Representations
Compressive Visual Representations
Kuang-Huei Lee, Anurag Arnab, Sergio Guadarrama, John Canny, Ian Fischer
Learning effective visual representations that generalize well without human supervision is a fundamental problem in order to apply Machine Learning to a wide variety of tasks. Recently, two families of self-supervised methods, contrastive learning and latent bootstrapping, exemplified by SimCLR and BYOL respectively, have made significant progress. In this work, we hypothesize that adding explicit information compression to these algorithms yields better and more robust representations. We verify this by developing SimCLR and BYOL formulations compatible with the Conditional Entropy Bottleneck (CEB) objective, allowing us to both measure and control the amount of compression in the learned representation, and observe their impact on downstream tasks. Furthermore, we explore the relationship between Lipschitz continuity and compression, showing a tractable lower bound on the Lipschitz constant of the encoders we learn. As Lipschitz continuity is closely related to robustness, this provides a new explanation for why compressed models are more robust. Our experiments confirm that adding compression to SimCLR and BYOL significantly improves linear evaluation accuracies and model robustness across a wide range of domain shifts. In particular, the compressed version of BYOL achieves 76.0% Top-1 linear evaluation accuracy on ImageNet with ResNet-50, and 78.8% with ResNet-50 2x.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | ObjectNet | C-BYOL | Top-1 Accuracy | 25.5 | #81 of 106 | Archive leaderboard | report |
| Image Classification | ObjectNet | C-SimCLR | Top-1 Accuracy | 20.8 | #89 of 106 | Archive leaderboard | report |
| Self-Supervised Image Classification | ImageNet | C-BYOL (ResNet-50 2x, 1000 epochs) | Number of Params | 94M | #41 of 144 | Archive leaderboard | report |
| Self-Supervised Image Classification | ImageNet | C-BYOL (ResNet-50 2x, 1000 epochs) | Top 1 Accuracy | 78.8% | #41 of 144 | Archive leaderboard | report |
| Self-Supervised Image Classification | ImageNet | C-BYOL (ResNet-50 2x, 1000 epochs) | Top 5 Accuracy | 94.5% | #41 of 144 | Archive leaderboard | report |
| Self-Supervised Image Classification | ImageNet | C-BYOL (ResNet-50, 1000 epochs) | Number of Params | 25M | #70 of 144 | Archive leaderboard | report |
| Self-Supervised Image Classification | ImageNet | C-BYOL (ResNet-50, 1000 epochs) | Top 1 Accuracy | 75.6% | #70 of 144 | Archive leaderboard | report |
| Self-Supervised Image Classification | ImageNet | C-BYOL (ResNet-50, 1000 epochs) | Top 5 Accuracy | 92.7% | #70 of 144 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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