  Multi-view or even multi-modal data is appealing yet challenging for
real-world applications. Detecting anomalies in multi-view data is a prominent
recent research topic. However, most of the existing methods 1) are only
suitable for two views or type-specific anomalies, 2) suffer from the issue of
fusion disentanglement, and 3) do not support online detection after model
deployment. To address these challenges, our main ideas in this paper are
three-fold: multi-view learning, disentangled representation learning, and
generative model. To this end, we propose dPoE, a novel multi-view variational
autoencoder model that involves (1) a Product-of-Experts (PoE) layer in
tackling multi-view data, (2) a Total Correction (TC) discriminator in
disentangling view-common and view-specific representations, and (3) a joint
loss function in wrapping up all components. In addition, we devise theoretical
information bounds to control both view-common and view-specific
representations. Extensive experiments on six real-world datasets markedly
demonstrate that the proposed dPoE outperforms baselines.
