Iterative Learning with Open-set Noisy Labels

Abstract

Large-scale datasets possessing clean label annotations are crucial for training Convolutional Neural Networks (CNNs). However, labeling large-scale data can be very costly and error-prone, and even high-quality datasets are likely to contain noisy (incorrect) labels. Existing works usually employ a closed-set assumption, whereby the samples associated with noisy labels possess a true class contained within the set of known classes in the training data. However, such an assumption is too restrictive for many applications, since samples associated with noisy labels might in fact possess a true class that is not present in the training data. We refer to this more complex scenario as the \textbf{open-set noisy label} problem and show that it is nontrivial in order to make accurate predictions. To address this problem, we propose a novel iterative learning framework for training CNNs on datasets with open-set noisy labels. Our approach detects noisy labels and learns deep discriminative features in an iterative fashion. To benefit from the noisy label detection, we design a Siamese network to encourage clean labels and noisy labels to be dissimilar. A reweighting module is also applied to simultaneously emphasize the learning from clean labels and reduce the effect caused by noisy labels. Experiments on CIFAR-10, ImageNet and real-world noisy (web-search) datasets demonstrate that our proposed model can robustly train CNNs in the presence of a high proportion of open-set as well as closed-set noisy labels.

arXiv commentCVPR2018-Spotlight

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• Image Captioning with Very Scarce Supervised Data: Adversarial Semi-Supervised Learning Approach
• Training Binary Neural Networks through Learning with Noisy Supervision
• Learning with Bounded Instance- and Label-dependent Label Noise
• Robust Learning Under Label Noise With Iterative Noise-Filtering
• Understanding the Interaction of Adversarial Training with Noisy Labels
• On Universal Black-Box Domain Adaptation
• Weakly Supervised Learning with Side Information for Noisy Labeled Images
• Fast and Exact Outlier Detection in Metric Spaces: A Proximity Graph-based Approach
• NLNL: Negative Learning for Noisy Labels
• Robust Deep Graph Based Learning for Binary Classification
• Towards Robust Pattern Recognition: A Review
• Alternating Loss Correction for Preterm-Birth Prediction from EHR Data with Noisy Labels
• Reliable Label Bootstrapping for Semi-Supervised Learning
• Do We Really Need Gold Samples for Sample Weighting Under Label Noise?