Task agnostic robust learning on corrupt outputs by correlation-guided mixture density networks

Sungjoon Choi, Sanghoon Hong, Sungbin Lim, Kyungjae Lee

Research output: Contribution to journalConference articlepeer-review

Abstract

In this paper, we focus on weakly supervised learning with noisy training data for both classification and regression problems. We assume that the training outputs are collected from a mixture of a target and correlated noise distributions. Our proposed method simultaneously estimates the target distribution and the quality of each data which is defined as the correlation between the target and data generating distributions. The cornerstone of the proposed method is a Cholesky Block that enables modeling dependencies among mixture distributions in a differentiable manner where we maintain the distribution over the network weights. We first provide illustrative examples in both regression and classification tasks to show the effectiveness of the proposed method. Then, the proposed method is extensively evaluated in a number of experiments where we show that it constantly shows comparable or superior performances compared to existing baseline methods in the handling of noisy data.

Original languageEnglish
Article number9156937
Pages (from-to)3871-3880
Number of pages10
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 - Virtual, Online, United States
Duration: 2020 Jun 142020 Jun 19

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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