 approach%2C where the out-of-distribution data is a huge collective of random images%2C or as a fully self-supervised learning approach%2C where the out-of-distribution data is self-generated by applying distribution-shifting transformations. The ability to incorporate additional out-of-distribution samples enables a feasible solution for datasets where AD methods based on contrastive learning generally underperform%2C such as aerial images or microscopy images. Furthermore%2C the high-quality features learned through contrastive learning consistently enhance performance in OE scenarios%2C even when the available out-of-distribution dataset is not diverse enough. Our extensive experiments demonstrate the superiority of our proposed method under various scenarios%2C including unimodal and multimodal settings%2C with various image datasets.?quality=80&w=1200)
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