Unified Robust Semi-Supervised Variational Autoencoder

Xu Chen
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:1529-1538, 2021.

Abstract

In this paper, we propose a novel noise-robust semi-supervised deep generative model by jointly tackling noisy labels and outliers simultaneously in a unified robust semi-supervised variational autoencoder (URSVAE). Typically, the uncertainty of of input data is characterized by placing uncertainty prior on the parameters of the probability density distributions in order to ensure the robustness of the variational encoder towards outliers. Subsequently, a noise transition model is integrated naturally into our model to alleviate the detrimental effects of noisy labels. Moreover, a robust divergence measure is employed to further enhance the robustness, where a novel variational lower bound is derived and optimized to infer the network parameters. By proving the influence function on the proposed evidence lower bound is bounded, the enormous potential of the proposed model in the classification in the presence of the compound noise is demonstrated. The experimental results highlight the superiority of the proposed framework by the evaluating on image classification tasks and comparing with the state-of-the-art approaches.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-chen21a, title = {Unified Robust Semi-Supervised Variational Autoencoder}, author = {Chen, Xu}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {1529--1538}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/chen21a/chen21a.pdf}, url = {https://proceedings.mlr.press/v139/chen21a.html}, abstract = {In this paper, we propose a novel noise-robust semi-supervised deep generative model by jointly tackling noisy labels and outliers simultaneously in a unified robust semi-supervised variational autoencoder (URSVAE). Typically, the uncertainty of of input data is characterized by placing uncertainty prior on the parameters of the probability density distributions in order to ensure the robustness of the variational encoder towards outliers. Subsequently, a noise transition model is integrated naturally into our model to alleviate the detrimental effects of noisy labels. Moreover, a robust divergence measure is employed to further enhance the robustness, where a novel variational lower bound is derived and optimized to infer the network parameters. By proving the influence function on the proposed evidence lower bound is bounded, the enormous potential of the proposed model in the classification in the presence of the compound noise is demonstrated. The experimental results highlight the superiority of the proposed framework by the evaluating on image classification tasks and comparing with the state-of-the-art approaches.} }
Endnote
%0 Conference Paper %T Unified Robust Semi-Supervised Variational Autoencoder %A Xu Chen %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-chen21a %I PMLR %P 1529--1538 %U https://proceedings.mlr.press/v139/chen21a.html %V 139 %X In this paper, we propose a novel noise-robust semi-supervised deep generative model by jointly tackling noisy labels and outliers simultaneously in a unified robust semi-supervised variational autoencoder (URSVAE). Typically, the uncertainty of of input data is characterized by placing uncertainty prior on the parameters of the probability density distributions in order to ensure the robustness of the variational encoder towards outliers. Subsequently, a noise transition model is integrated naturally into our model to alleviate the detrimental effects of noisy labels. Moreover, a robust divergence measure is employed to further enhance the robustness, where a novel variational lower bound is derived and optimized to infer the network parameters. By proving the influence function on the proposed evidence lower bound is bounded, the enormous potential of the proposed model in the classification in the presence of the compound noise is demonstrated. The experimental results highlight the superiority of the proposed framework by the evaluating on image classification tasks and comparing with the state-of-the-art approaches.
APA
Chen, X.. (2021). Unified Robust Semi-Supervised Variational Autoencoder. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:1529-1538 Available from https://proceedings.mlr.press/v139/chen21a.html.

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