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.
@article{arxiv.1809.05957,
title = {A Deep Generative Model for Semi-Supervised Classification with Noisy Labels},
author = {Maxime Langevin and Edouard Mehlman and Jeffrey Regier and Romain Lopez and Michael I. Jordan and Nir Yosef},
journal= {arXiv preprint arXiv:1809.05957},
year = {2018}
}