Papers › Low-shot learning with large-scale diffusion
Low-shot learning with large-scale diffusion
Matthijs Douze, Arthur Szlam, Bharath Hariharan, Hervé Jégou
This paper considers the problem of inferring image labels from images when only a few annotated examples are available at training time. This setup is often referred to as low-shot learning, where a standard approach is to re-train the last few layers of a convolutional neural network learned on separate classes for which training examples are abundant. We consider a semi-supervised setting based on a large collection of images to support label propagation. This is possible by leveraging the recent advances on large-scale similarity graph construction. We show that despite its conceptual simplicity, scaling label propagation up to hundred millions of images leads to state of the art accuracy in the low-shot learning regime.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Few-Shot Image Classification | ImageNet-FS (1-shot, novel) | LSD (ResNet-50) | Top-5 Accuracy (%) | 57.7 | #6 of 7 | Archive leaderboard | report |
| Few-Shot Image Classification | ImageNet-FS (2-shot, novel) | LSD (ResNet-50) | Top-5 Accuracy (%) | 66.9 | #7 of 8 | Archive leaderboard | report |
| Few-Shot Image Classification | ImageNet-FS (5-shot, all) | LSD (ResNet-50) | Top-5 Accuracy (%) | 73.8 | #8 of 8 | 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.
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