# Learning to Learn from Noisy Labeled Data

## TL;DR

This paper introduces a meta-learning based training algorithm that enhances deep neural networks' robustness to noisy labels, improving performance on datasets with label inaccuracies.

## Contribution

It proposes a novel noise-tolerant training method using meta-learning to prevent overfitting to label noise, validated through extensive experiments.

## Key findings

- Outperforms state-of-the-art methods on noisy CIFAR-10
- Achieves superior accuracy on Clothing1M dataset
- Demonstrates robustness to high levels of label noise

## Abstract

Despite the success of deep neural networks (DNNs) in image classification tasks, the human-level performance relies on massive training data with high-quality manual annotations, which are expensive and time-consuming to collect. There exist many inexpensive data sources on the web, but they tend to contain inaccurate labels. Training on noisy labeled datasets causes performance degradation because DNNs can easily overfit to the label noise. To overcome this problem, we propose a noise-tolerant training algorithm, where a meta-learning update is performed prior to conventional gradient update. The proposed meta-learning method simulates actual training by generating synthetic noisy labels, and train the model such that after one gradient update using each set of synthetic noisy labels, the model does not overfit to the specific noise. We conduct extensive experiments on the noisy CIFAR-10 dataset and the Clothing1M dataset. The results demonstrate the advantageous performance of the proposed method compared to several state-of-the-art baselines.