Exploring using jigsaw puzzles for out-of-distribution detection

Out-of-distribution (OOD) detection involves binary classification whether the given data is from outside the training data or not. Previous studies proposed outlier exposure (OE) that trains the model on an outlier dataset designed to represent potential future OOD data, thereby enhancing OOD detection performance. However, obtaining an outlier dataset representing all possible future OOD data can be challenging, and such dataset may be unavailable in some cases. This study proposes a novel approach to expose the model to jigsaw puzzles generated from training images as the outlier data. Specifically, the model is trained to have a low LogitNorm for given jigsaw puzzles. We argue that jigsaw puzzles can effectively represent future OOD data because they contain similar background information as the in-distribution data but with their semantic information destroyed. Our experimental results demonstrate that our approach outperforms previous competitive OOD detection methods and effectively detects semantically shifted OOD examples. Our code is available at https://github.com/gist-ailab/jigsaw-training-OOD.