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Using Early-Learning Regularization to Classify Real-World Noisy Data

2021-05-27 15:41:45
Alessio Galatolo, Alfred Nilsson, Roderick Karlemstrand, Yineng Wang
arXiv_CV
         
Abstract

The memorization problem is well-known in the field of computer vision. Liu et al. propose a technique called Early-Learning Regularization, which improves accuracy on the CIFAR datasets when label noise is present. This project replicates their experiments and investigates the performance on a real-world dataset with intrinsic noise. Results show that their experimental results are consistent. We also explore Sharpness-Aware Minimization in addition to SGD and observed a further 14.6 percentage points improvement. Future work includes using all 6 million images and manually clean a fraction of the images to fine-tune a transfer learning model. Last but not the least, having access to clean data for testing would also improve the measurement of accuracy.

Abstract (translated)

URL

https://arxiv.org/abs/2105.13244

PDF

https://arxiv.org/pdf/2105.13244.pdf

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