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Deep neural networks have demonstrated prominent capacities for imageclassification tasks in a closed set setting, where the test data come from thesame distribution as the training data. However, in a more realistic open setscenario, traditional classifiers with incomplete knowledge cannot tackle testdata that are not from the training classes. Open set recognition (OSR) aims toaddress this problem by both identifying unknown classes and distinguishingknown classes simultaneously. In this paper, we propose a novel approach to OSRthat is based on the vision transformer (ViT) technique. Specifically, ourapproach employs two separate training stages. First, a ViT model is trained toperform closed set classification. Then, an additional detection head isattached to the embedded features extracted by the ViT, trained to force therepresentations of known data to class-specific clusters compactly. Testexamples are identified as known or unknown based on their distance to thecluster centers. To the best of our knowledge, this is the first time toleverage ViT for the purpose of OSR, and our extensive evaluation againstseveral OSR benchmark datasets reveals that our approach significantlyoutperforms other baseline methods and obtains new state-of-the-artperformance.

Open Set Recognition using Vision Transformer with an Additional Detection Head