Robust Learning Under Label Noise With Iterative Noise-Filtering

Understand

We consider the problem of training a model under the presence of label noise.

• Current approaches identify samples with potentially incorrect labels and reduce their influence on the learning process by either assigning lower weights to them or completely removing them from the training set.
• In the first case the model however still learns from noisy labels; in the latter approach, good training data can be lost.
• In this paper, we propose an iterative semi-supervised mechanism for robust learning which excludes noisy labels but is still able to learn from the corresponding samples.