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

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