Research map / Label-efficient and robust learning / Self-supervised and contrastive
A theoretical study of inductive biases in contrastive learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase the requested partition size from m=10 to m=300 | ResNet-18 · CIFAR-10 test | phi_m: 0.131 -> 0.521 | Hurt |
| Increase the requested partition size from m=10 to m=300 | ResNet-101 · CIFAR-10 test | phi_m: 0.053 -> 0.459 | Hurt |
| Increase the requested partition size from m=10 to m=300 | WRN · CIFAR-10 test | phi_m: 0.080 -> 0.340 | Hurt |
| Increase the requested partition size from m=10 to m=300 | ViT · CIFAR-10 test | phi_m: 0.072 -> 0.389 | Hurt |
| Use ResNet-101 instead of ResNet-18 | CIFAR-10 contrastive model · CIFAR-10 test, m=10 | phi_m: 0.131 -> 0.053 | Helped |
| Use WRN instead of ResNet-18 | CIFAR-10 contrastive model · CIFAR-10 test, m=10 | phi_m: 0.131 -> 0.080 | Helped |
| Use ViT instead of ResNet-18 | CIFAR-10 contrastive model · CIFAR-10 test, m=10 | phi_m: 0.131 -> 0.072 | Helped |
Closest papers by meaning
- On the duality between contrastive and non-contrastive self-supervised learning (ICLR 2023 · similarity 0.91)
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- When Does Contrastive Visual Representation Learning Work? (CVPR 2022 · similarity 0.90)
- An Investigation of Representation and Allocation Harms in Contrastive Learning (ICLR 2024 · similarity 0.89)
- Self-Supervised Contrastive Learning is Approximately Supervised Contrastive Learning (NeurIPS 2025 · similarity 0.89)
- Collapse-Proof Non-Contrastive Self-Supervised Learning (ICML 2025 · similarity 0.88)
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