  Out-of-distribution (OOD) detection is crucial to safety-critical machine
learning applications and has been extensively studied. While recent studies
have predominantly focused on classifier-based methods, research on deep
generative model (DGM)-based methods have lagged relatively. This disparity may
be attributed to a perplexing phenomenon: DGMs often assign higher likelihoods
to unknown OOD inputs than to their known training data. This paper focuses on
explaining the underlying mechanism of this phenomenon. We propose a hypothesis
that less complex images concentrate in high-density regions in the latent
space, resulting in a higher likelihood assignment in the Normalizing Flow
(NF). We experimentally demonstrate its validity for five NF architectures,
concluding that their likelihood is untrustworthy. Additionally, we show that
this problem can be alleviated by treating image complexity as an independent
variable. Finally, we provide evidence of the potential applicability of our
hypothesis in another DGM, PixelCNN++.
