Research map / Label-efficient and robust learning / Recognition and long-tail
Do More Negative Samples Necessarily Hurt In Contrastive Learning?
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase the number of negative samples from k=1 to k=512 | Contrastive learning on CIFAR-10 · CIFAR-10 | downstream classification accuracy | Helped |
| Increase the number of negative samples k | Population NCE-optimal representation with non-overlapping, uniformly distributed latent classes · Uniform latent classes | downstream supervised loss | No clear effect |
| Increase the number of negative samples k | Numerically computed population NCE-optimal representation · Non-uniform latent classes, C in {3, 9} | downstream supervised loss | Mixed |
| Train with CIFAR-100 data under the paper's positive-pair setup | Contrastive learning · CIFAR-100 test | downstream classification accuracy: 64.76% | Helped |
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