  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.
