A Deep Generative Model for Semi-Supervised Classification with Noisy Labels

Maxime Langevin; Edouard Mehlman; Jeffrey Regier; Romain Lopez; Michael I Jordan; Nir Yosef

doi:https://doi.org/10.48550/arxiv.1809.05957

A Deep Generative Model for Semi-Supervised Classification with Noisy Labels

Maxime Langevin, Edouard Mehlman, Jeffrey Regier, Romain Lopez, Michael I Jordan, Nir Yosef

Research output: Contribution to journal › Article

Abstract

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.

Original language	English
Number of pages	3
Journal	arxiv.org
DOIs	https://doi.org/10.48550/arxiv.1809.05957
State	In preparation - 16 Sep 2018
Externally published	Yes

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title = "A Deep Generative Model for Semi-Supervised Classification with Noisy Labels",

abstract = "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.",

author = "Maxime Langevin and Edouard Mehlman and Jeffrey Regier and Romain Lopez and Jordan, {Michael I} and Nir Yosef",

year = "2018",

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AU - Langevin, Maxime

AU - Mehlman, Edouard

AU - Regier, Jeffrey

AU - Lopez, Romain

AU - Jordan, Michael I

AU - Yosef, Nir

PY - 2018/9/16

Y1 - 2018/9/16

N2 - 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.

AB - 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.

U2 - https://doi.org/10.48550/arxiv.1809.05957

DO - https://doi.org/10.48550/arxiv.1809.05957

M3 - مقالة

SN - 2331-8422

JO - arxiv.org

JF - arxiv.org

ER -

A Deep Generative Model for Semi-Supervised Classification with Noisy Labels

Abstract

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