  This paper proposes a novel out-of-distribution (OOD) detection framework
named MoodCat for image classifiers. MoodCat masks a random portion of the
input image and uses a generative model to synthesize the masked image to a new
image conditioned on the classification result. It then calculates the semantic
difference between the original image and the synthesized one for OOD
detection. Compared to existing solutions, MoodCat naturally learns the
semantic information of the in-distribution data with the proposed mask and
conditional synthesis strategy, which is critical to identifying OODs.
Experimental results demonstrate that MoodCat outperforms state-of-the-art OOD
detection solutions by a large margin.
