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Autoencoder, as an essential part of many anomaly detection methods, islacking flexibility on normal data in complex datasets. U-Net is proved to beeffective for this purpose but overfits on the training data if trained by justusing reconstruction error similar to other AE-based frameworks.Puzzle-solving, as a pretext task of self-supervised learning (SSL) methods,has earlier proved its ability in learning semantically meaningful features. Weshow that training U-Nets based on this task is an effective remedy thatprevents overfitting and facilitates learning beyond pixel-level features.Shortcut solutions, however, are a big challenge in SSL tasks, including jigsawpuzzles. We propose adversarial robust training as an effective automaticshortcut removal. We achieve competitive or superior results compared to theState of the Art (SOTA) anomaly detection methods on various toy and real-worlddatasets. Unlike many competitors, the proposed framework is stable, fast,data-efficient, and does not require unprincipled early stopping.

Puzzle-AE: Novelty Detection in Images through Solving Puzzles