Papers › Inpainting Transformer for Anomaly Detection
Inpainting Transformer for Anomaly Detection
Jonathan Pirnay, Keng Chai
Anomaly detection in computer vision is the task of identifying images which deviate from a set of normal images. A common approach is to train deep convolutional autoencoders to inpaint covered parts of an image and compare the output with the original image. By training on anomaly-free samples only, the model is assumed to not being able to reconstruct anomalous regions properly. For anomaly detection by inpainting we suggest it to be beneficial to incorporate information from potentially distant regions. In particular we pose anomaly detection as a patch-inpainting problem and propose to solve it with a purely self-attention based approach discarding convolutions. The proposed Inpainting Transformer (InTra) is trained to inpaint covered patches in a large sequence of image patches, thereby integrating information across large regions of the input image. When training from scratch, in comparison to other methods not using extra training data, InTra achieves results on par with the current state-of-the-art on the MVTec AD dataset for detection and surpassing them on segmentation.
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Code
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Code Syntology ran Syntology
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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 | AeBAD-S | InTra | Detection AUROC | 44.2 | #7 of 8 | Archive leaderboard | report |
| Anomaly Detection | AeBAD-S | InTra | Segmentation AUPRO | 74.7 | #7 of 8 | Archive leaderboard | report |
| Anomaly Detection | AeBAD-V | InTra | Detection AUROC | 54.1 | #7 of 7 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | InTra | Detection AUROC | 95.0 | #94 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | InTra | Segmentation AUROC | 96.6 | #94 of 148 | 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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