IEG: Robust Neural Network Training to Tackle Severe Label Noise
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Collecting large-scale data with clean labels for supervised training of neural networks is practically challenging. Although noisy labels are usually cheap to acquire, existing methods suffer severely for training datasets with high noise ratios, making high-cost human labeling a necessity. Here we present a method to train neural networks in a way that is almost invulnerable to severe label noise by utilizing a tiny trusted set. Our method, named IEG, is based on three key insights: (i) Isolation of noisy labels, (ii) Escalation of useful supervision from mislabeled data, and (iii) Guidance from small trusted data. On CIFAR100 with a 40 per class, our method achieves 80.2±0.3% classification accuracy, only 1.4 increasing the noise ratio to 80 75.5±0.2%, compared to the previous best 47.7 new state of the art on various types of challenging label corruption types and levels and large-scale WebVision benchmarks.
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