Research map / Label-efficient and robust learning / Semi-supervised learning
CO2: Consistent Contrast for Unsupervised Visual Representation Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add CO2 consistency regularization | MoCo · ImageNet-1K val, linear classification | Top-1 Acc. (%): 60.6 -> 63.5; 2.9% | Helped |
| Add CO2 consistency regularization | MoCo v2 · ImageNet-1K val, linear classification | Top-1 Acc. (%): 67.5 -> 68.0; 0.5% | Helped |
| Add CO2 consistency regularization | MoCo · ImageNet-1K val, 1% labels | Top-5 accuracy: 62.4 -> 66.2; 3.8% | Helped |
| Add CO2 consistency regularization | MoCo · ImageNet-1K val, 10% labels | Top-5 accuracy: 84.1 -> 85.2; 1.2% | Helped |
| Add CO2 consistency regularization | MoCo v2 · ImageNet-1K val, 1% labels | Top-5 accuracy: 69.8 -> 71.0; 1.2% | Helped |
| Add CO2 consistency regularization | MoCo v2 · ImageNet-1K val, 10% labels | Top-5 accuracy: 85.0 -> 85.7; 0.7% | Helped |
| Add CO2 consistency regularization | MoCo · PASCAL VOC, image classification | mAP: 79.7 -> 82.6; 2.9% | Helped |
| Add CO2 consistency regularization | MoCo v2 · PASCAL VOC, image classification | mAP: 85.0 -> 85.2; 0.2% | Helped |
| Add CO2 consistency regularization | MoCo · PASCAL VOC, object detection | AP50: 81.6 -> 81.9; 0.3% | Helped |
| Add CO2 consistency regularization | MoCo · PASCAL VOC, object detection | APall: 56.2 -> 56.0 | Hurt |
| Add CO2 consistency regularization | MoCo · PASCAL VOC, semantic segmentation | mIoU: 72.6 -> 73.3; 0.9% | Helped |
| Add consistency regularization with α=10 (ablation) | MoCo, ResNet-18 · ImageNet-100, linear classification | Top-1 accuracy: 63.6% -> 69.2% | Helped |
Closest papers by meaning
- Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations (NeurIPS 2021 · similarity 0.88)
- Momentum Contrast for Unsupervised Visual Representation Learning (CVPR 2020 · similarity 0.88)
- Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning (CVPR 2021 · similarity 0.87)
- A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning (NeurIPS 2021 · similarity 0.86)
- Supervised Contrastive Learning (NeurIPS 2020 · similarity 0.86)
- $\mathbb{X}$-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs (ICLR 2025 · similarity 0.84)
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