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Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels

Lu Jiang
Di Huang
Mason Liu
ICML (2020)


Performing controlled experiments on noisy data is essential in understanding deep learning across noise levels. Due to the lack of suitable datasets, previous research has only examined deep learning on controlled synthetic label noise, and real-world label noise has never been studied in a controlled setting. This paper makes three contributions. First, we establish the first benchmark of controlled real label noise (obtained from image search). This new benchmark will enable us to study the image search label noise in a controlled setting for the first time. The second contribution is a simple but highly effective method to overcome both synthetic and real noisy labels. We show that our method achieves the best result on our dataset as well as on two public benchmarks (CIFAR and WebVision). Third, we conduct the largest study by far into understanding deep neural networks trained on noisy labels across different noise levels, noise types, network architectures, methods, and training settings. We will release the data and code to reproduce our results.