Papers › FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction
FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction
Zheng Fang, Xiaoyang Wang, Haocheng Li, Jiejie Liu, Qiugui Hu, Jimin Xiao
In industrial anomaly detection, data efficiency and the ability for fast migration across products become the main concerns when developing detection algorithms. Existing methods tend to be data-hungry and work in the one-model-one-category way, which hinders their effectiveness in real-world industrial scenarios. In this paper, we propose a few-shot anomaly detection strategy that works in a low-data regime and can generalize across products at no cost. Given a defective query sample, we propose to utilize a few normal samples as a reference to reconstruct its normal version, where the final anomaly detection can be achieved by sample alignment. Specifically, we introduce a novel regression with distribution regularization to obtain the optimal transformation from support to query features, which guarantees the reconstruction result shares visual similarity with the query sample and meanwhile maintains the property of normal samples. Experimental results reflect that our method significantly outperforms previous state-of-the-art at both image and pixel-level AUROC performances from 2 to 8-shot scenarios. Besides, with only a limited number of training samples (less than 8 samples), our method reaches competitive performance with vanilla AD methods which are trained with extensive normal samples.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Detection | MPDD | FastRecon | Detection AUROC | 82.5 | #12 of 16 | Archive leaderboard | report |
| Anomaly Detection | MPDD | FastRecon | Segmentation AUROC | 97.9 | #12 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.
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