  AI Safety is a major concern in many deep learning applications such as
autonomous driving. Given a trained deep learning model, an important natural
problem is how to reliably verify the model's prediction. In this paper, we
propose a novel framework -- deep verifier networks (DVN) to verify the inputs
and outputs of deep discriminative models with deep generative models. Our
proposed model is based on conditional variational auto-encoders with
disentanglement constraints. We give both intuitive and theoretical
justifications of the model. Our verifier network is trained independently with
the prediction model, which eliminates the need of retraining the verifier
network for a new model. We test the verifier network on out-of-distribution
detection and adversarial example detection problems, as well as anomaly
detection problems in structured prediction tasks such as image caption
generation. We achieve state-of-the-art results in all of these problems.
