Self-Supervised Out-of-Distribution Detection and Localization with Natural Synthetic Anomalies (NSA)
Abstract: We introduce a new self-supervised task, NSA, for training an end-to-end model for anomaly detection and localization using only normal data. NSA uses Poisson image editing to seamlessly blend scaled patches of various sizes from separate images. This creates a wide range of synthetic anomalies which are more similar to natural sub-image irregularities than previous data-augmentation strategies for self-supervised anomaly detection. We evaluate the proposed method using natural and medical images. Our experiments with the MVTec AD dataset show that a model trained to localize NSA anomalies generalizes well to detecting real-world a priori unknown types of manufacturing defects. Our method achieves an overall detection AUROC of 97.2 outperforming all previous methods that learn from scratch without pre-training datasets.
Please see our arXiv preprint for more details: https://arxiv.org/abs/2109.15222.
The NIH chest X-ray data can be downloaded here.
The MVTec AD dataset can be downloaded here.
The method for generating self-supervised examples is defined in self_sup_data/self_sup_tasks.py. This is applied dynamically to the images while loading similar to other data-augmentation transforms (see the corresponding Dataset classes).
To train anomaly detection models for chest X-ray or MVTec AD data use train_mvtec.py and train_chest_xray.py. E.g.
python3 train_chest_xray.py -s Shift-Intensity-M -d '/path/to/cxr/images/' -o '/where/to/save/models/' -l 'self_sup_data/chest_xray_lists/norm_MaleAdultPA_train_curated_list.txt'
python3 train_mvtec.py -s Shift-Intensity-923874273 -d '/path/to/mvtec_ad/images/' -o '/where/to/save/models/' -n zipper
The evaluation procedures are defined in experiments/mvtec_tasks.py and experiments/chest_xray_tasks.py. The evaluation notebooks (mvtec_evaluation.ipynb and chestxray_evaluation.ipynb) can be used to run the evaluation and generate tables for sample-level and pixel-level AUROC and AU-PRO where applicable.