We propose negative sampling as an approach to improve the notoriously bad out-of-distribution likelihood estimates of Variational Autoencoder models. Our model pushes latent images of negative samples away from the prior. When the source of negative samples is an auxiliary dataset, such a model can vastly improve on baselines when evaluated on OOD detection tasks. Perhaps more surprisingly, we present a fully unsupervised version of employing negative sampling in VAEs: when the generator is trained in an adversarial manner, using the generator's own outputs as negative samples can also significantly improve the robustness of OOD likelihood estimates.4th workshop on Bayesian Deep Learning (NeurIPS 2019),
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