Open-Set Recognition with Gaussian Mixture Variational Autoencoders

• URL: http://arxiv.org/abs/2006.02003v1
• Date: Wed, 3 Jun 2020 01:15:19 GMT
• Title: Open-Set Recognition with Gaussian Mixture Variational Autoencoders
• Authors: Alexander Cao, Yuan Luo, Diego Klabjan
• Abstract summary: In inference, open-set classification is to either classify a sample into a known class from training or reject it as an unknown class. We train our model to cooperatively learn reconstruction and perform class-based clustering in the latent space. Our model achieves more accurate and robust open-set classification results, with an average F1 improvement of 29.5%.
• Score: 91.3247063132127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In inference, open-set classification is to either classify a sample into a known class from training or reject it as an unknown class. Existing deep open-set classifiers train explicit closed-set classifiers, in some cases disjointly utilizing reconstruction, which we find dilutes the latent representation's ability to distinguish unknown classes. In contrast, we train our model to cooperatively learn reconstruction and perform class-based clustering in the latent space. With this, our Gaussian mixture variational autoencoder (GMVAE) achieves more accurate and robust open-set classification results, with an average F1 improvement of 29.5%, through extensive experiments aided by analytical results.

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