Papers › MoPro: Webly Supervised Learning with Momentum Prototypes
MoPro: Webly Supervised Learning with Momentum Prototypes
Junnan Li, Caiming Xiong, Steven C. H. Hoi
We propose a webly-supervised representation learning method that does not suffer from the annotation unscalability of supervised learning, nor the computation unscalability of self-supervised learning. Most existing works on webly-supervised representation learning adopt a vanilla supervised learning method without accounting for the prevalent noise in the training data, whereas most prior methods in learning with label noise are less effective for real-world large-scale noisy data. We propose momentum prototypes (MoPro), a simple contrastive learning method that achieves online label noise correction, out-of-distribution sample removal, and representation learning. MoPro achieves state-of-the-art performance on WebVision, a weakly-labeled noisy dataset. MoPro also shows superior performance when the pretrained model is transferred to down-stream image classification and detection tasks. It outperforms the ImageNet supervised pretrained model by +10.5 on 1-shot classification on VOC, and outperforms the best self-supervised pretrained model by +17.3 when finetuned on 1\% of ImageNet labeled samples. Furthermore, MoPro is more robust to distribution shifts. Code and pretrained models are available at https://github.com/salesforce/MoPro.
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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 | OmniBenchmark | MoPro-V2 | Average Top-1 Accuracy | 36.1 | #12 of 22 | Archive leaderboard | report |
| Image Classification | WebVision-1000 | MoPro (ResNet-50) | ImageNet Top-1 Accuracy | 67.8% | #13 of 16 | Archive leaderboard | report |
| Image Classification | WebVision-1000 | MoPro (ResNet-50) | ImageNet Top-5 Accuracy | 87.0% | #13 of 16 | Archive leaderboard | report |
| Image Classification | WebVision-1000 | MoPro (ResNet-50) | Top-1 Accuracy | 73.9% | #13 of 16 | Archive leaderboard | report |
| Image Classification | WebVision-1000 | MoPro (ResNet-50) | Top-5 Accuracy | 90.0% | #13 of 16 | 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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