NLNL: Negative Learning for Noisy Labels

Abstract

Convolutional Neural Networks (CNNs) provide excellent performance when used for image classification. The classical method of training CNNs is by labeling images in a supervised manner as in "input image belongs to this label" (Positive Learning; PL), which is a fast and accurate method if the labels are assigned correctly to all images. However, if inaccurate labels, or noisy labels, exist, training with PL will provide wrong information, thus severely degrading performance. To address this issue, we start with an indirect learning method called Negative Learning (NL), in which the CNNs are trained using a complementary label as in "input image does not belong to this complementary label." Because the chances of selecting a true label as a complementary label are low, NL decreases the risk of providing incorrect information. Furthermore, to improve convergence, we extend our method by adopting PL selectively, termed as Selective Negative Learning and Positive Learning (SelNLPL). PL is used selectively to train upon expected-to-be-clean data, whose choices become possible as NL progresses, thus resulting in superior performance of filtering out noisy data. With simple semi-supervised training technique, our method achieves state-of-the-art accuracy for noisy data classification, proving the superiority of SelNLPL's noisy data filtering ability.

arXiv commentICCV 2019, Accepted

References in corpus (17)

• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• Wide Residual Networks
• Learning to Reweight Examples for Robust Deep Learning
• Training Deep Neural Networks on Noisy Labels with Bootstrapping
• MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
• Training Convolutional Networks with Noisy Labels
• Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
• Dimensionality-Driven Learning with Noisy Labels
• Making Risk Minimization Tolerant to Label Noise
• Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
• Learning From Noisy Singly-labeled Data
• Joint Optimization Framework for Learning with Noisy Labels
• Learning from Complementary Labels
• Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels
• CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise
• Iterative Learning with Open-set Noisy Labels
• A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels