Paper Info
Title
Masking: A New Perspective of Noisy Supervision.
Abstract
It is important to learn various types of classifiers given training data with noisy labels. Noisy labels, in the most popular noise model hitherto, are corrupted from ground-truth labels by an unknown noise transition matrix. Thus, by estimating this matrix, classifiers can escape from overfitting those noisy labels. However, such estimation is practically difficult, due to either the indirect nature of two-step approaches, or not big enough data to afford end-to-end approaches. In this paper, we propose a human-assisted approach called "Masking" that conveys human cognition of invalid class transitions and naturally speculates the structure of the noise transition matrix. To this end, we derive a structure-aware probabilistic model incorporating a structure prior, and solve the challenges from structure extraction and structure alignment. Thanks to Masking, we only estimate unmasked noise transition probabilities and the burden of estimation is tremendously reduced. We conduct extensive experiments on CIFAR-10 and CIFAR-100 with three noise structures as well as the industrial-level Clothing1M with agnostic noise structure, and the results show that Masking can improve the robustness of classifiers significantly.
Year
Venue
Keywords
2018
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018)
transition matrix,probabilistic model,new perspective,transition probability
DocType
Volume
ISSN
Conference
31
1049-5258
Citations 
PageRank 
References 
6
0.42
2
Authors
7
Name
Order
Citations
PageRank
Bo Han16123.20
Yao, Jiangchao2111.14
Gang Niu320436.78
Mingyuan Zhou463152.76
Ivor W. Tsang55396248.44
Ya Zhang6134091.72
Masashi Sugiyama73353264.24