CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise

TL;DR

CleanNet introduces a transfer learning approach that reduces human supervision in label noise cleaning for image classification, achieving significant noise detection improvements and high performance with minimal manual verification.

Contribution

The paper presents CleanNet, a neural embedding network that transfers label noise knowledge from a small verified class subset to others, enhancing scalable image classification with noisy labels.

Findings

Reduces label noise detection error rate by 41.5%.

Achieves 47% of full verification performance with only 3.2% images verified.

Effective on large-scale datasets for noisy data classification.

Abstract

In this paper, we study the problem of learning image classification models with label noise. Existing approaches depending on human supervision are generally not scalable as manually identifying correct or incorrect labels is time-consuming, whereas approaches not relying on human supervision are scalable but less effective. To reduce the amount of human supervision for label noise cleaning, we introduce CleanNet, a joint neural embedding network, which only requires a fraction of the classes being manually verified to provide the knowledge of label noise that can be transferred to other classes. We further integrate CleanNet and conventional convolutional neural network classifier into one framework for image classification learning. We demonstrate the effectiveness of the proposed algorithm on both of the label noise detection task and the image classification on noisy data task on several large-scale datasets. Experimental results show that CleanNet can reduce label noise detection error rate on held-out classes where no human supervision available by 41.5% compared to current weakly supervised methods. It also achieves 47% of the performance gain of verifying all images with only 3.2% images verified on an image classification task. Source code and dataset will be available at kuanghuei.github.io/CleanNetProject.