Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels

12/02/2020
by   Xiaobo Xia, et al.
0

The label noise transition matrix T, reflecting the probabilities that true labels flip into noisy ones, is of vital importance to model label noise and design statistically consistent classifiers. The traditional transition matrix is limited to model closed-set label noise, where noisy training data has true class labels within the noisy label set. It is unfitted to employ such a transition matrix to model open-set label noise, where some true class labels are outside the noisy label set. Thus when considering a more realistic situation, i.e., both closed-set and open-set label noise occurs, existing methods will undesirably give biased solutions. Besides, the traditional transition matrix is limited to model instance-independent label noise, which may not perform well in practice. In this paper, we focus on learning under the mixed closed-set and open-set label noise. We address the aforementioned issues by extending the traditional transition matrix to be able to model mixed label noise, and further to the cluster-dependent transition matrix to better approximate the instance-dependent label noise in real-world applications. We term the proposed transition matrix as the cluster-dependent extended transition matrix. An unbiased estimator (i.e., extended T-estimator) has been designed to estimate the cluster-dependent extended transition matrix by only exploiting the noisy data. Comprehensive synthetic and real experiments validate that our method can better model the mixed label noise, following its more robust performance than the prior state-of-the-art label-noise learning methods.

READ FULL TEXT

Authors

page 1

page 2

page 3

page 4

06/14/2020

Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning

The transition matrix, denoting the transition relationship from clean l...
11/29/2021

Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection

Recent studies on learning with noisy labels have shown remarkable perfo...
03/06/2019

Safeguarded Dynamic Label Regression for Generalized Noisy Supervision

Learning with noisy labels, which aims to reduce expensive labors on acc...
04/06/2022

Fundamental limits to learning closed-form mathematical models from data

Given a finite and noisy dataset generated with a closed-form mathematic...
11/11/2020

EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels

The efficacy of deep learning depends on large-scale data sets that have...
11/27/2017

Learning with Biased Complementary Labels

In this paper we study the classification problem in which we have acces...
01/30/2022

Do We Need to Penalize Variance of Losses for Learning with Label Noise?

Algorithms which minimize the averaged loss have been widely designed fo...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.