  Variational autoencoders (VAEs) are influential generative models with rich
representation capabilities from the deep neural network architecture and
Bayesian method. However, VAE models have a weakness that assign a higher
likelihood to out-of-distribution (OOD) inputs than in-distribution (ID)
inputs. To address this problem, a reliable uncertainty estimation is
considered to be critical for in-depth understanding of OOD inputs. In this
study, we propose an improved noise contrastive prior (INCP) to be able to
integrate into the encoder of VAEs, called INCPVAE. INCP is scalable, trainable
and compatible with VAEs, and it also adopts the merits from the INCP for
uncertainty estimation. Experiments on various datasets demonstrate that
compared to the standard VAEs, our model is superior in uncertainty estimation
for the OOD data and is robust in anomaly detection tasks. The INCPVAE model
obtains reliable uncertainty estimation for OOD inputs and solves the OOD
problem in VAE models.
