  Class labels are often imperfectly observed, due to mistakes and to genuine
ambiguity among classes. We propose a new semi-supervised deep generative model
that explicitly models noisy labels, called the Mislabeled VAE (M-VAE). The
M-VAE can perform better than existing deep generative models which do not
account for label noise. Additionally, the derivation of M-VAE gives new
theoretical insights into the popular M1+M2 semi-supervised model.
