Open-Set Recognition with Gaussian Mixture Variational Autoencoders

Authors

  • Alexander Cao Northwestern University
  • Yuan Luo Northwestern University
  • Diego Klabjan Northwestern University

Keywords:

Classification and Regression, Neural Generative Models & Autoencoders

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 increase of 0.26, through extensive experiments aided by analytical results.

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Published

2021-05-18

How to Cite

Cao, A., Luo, Y., & Klabjan, D. (2021). Open-Set Recognition with Gaussian Mixture Variational Autoencoders. Proceedings of the AAAI Conference on Artificial Intelligence, 35(8), 6877-6884. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16848

Issue

Section

AAAI Technical Track on Machine Learning I