Detecting Out-of-distribution Samples via Variational Auto-encoder with Reliable Uncertainty Estimation
This addresses the OOD detection problem in generative models for researchers and practitioners in machine learning, representing an incremental improvement over standard VAEs.
The paper tackled the problem of variational autoencoders (VAEs) assigning higher likelihood to out-of-distribution (OOD) inputs than in-distribution ones by proposing an improved noise contrastive prior (INCP) integrated into VAEs, called INCPVAE, which demonstrated superior uncertainty estimation for OOD data and robustness in anomaly detection tasks across various datasets.
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.