  Variational auto-encoders (VAEs) are an influential and generally-used class
of likelihood-based generative models in unsupervised learning. The
likelihood-based generative models have been reported to be highly robust to
the out-of-distribution (OOD) inputs and can be a detector by assuming that the
model assigns higher likelihoods to the samples from the in-distribution (ID)
dataset than an OOD dataset. However, recent works reported a phenomenon that
VAE recognizes some OOD samples as ID by assigning a higher likelihood to the
OOD inputs compared to the one from ID. In this work, we introduce a new model,
namely Bigeminal Priors Variational auto-encoder (BPVAE), to address this
phenomenon. The BPVAE aims to enhance the robustness of the VAEs by combing the
power of VAE with the two independent priors that belong to the training
dataset and simple dataset, which complexity is lower than the training
dataset, respectively. BPVAE learns two datasets'features, assigning a higher
likelihood for the training dataset than the simple dataset. In this way, we
can use BPVAE's density estimate for detecting the OOD samples. Quantitative
experimental results suggest that our model has better generalization
capability and stronger robustness than the standard VAEs, proving the
effectiveness of the proposed approach of hybrid learning by collaborative
priors. Overall, this work paves a new avenue to potentially overcome the OOD
problem via multiple latent priors modeling.
