Multi-label Learning from Noisy Labels with Non-linear Feature Transformation

Abstract

Multi-label classification is an essential problem in image classification, because there are usually multiple related tags associated with each image. However, building a large scale multi-label dataset with clean labels can be very expensive and difficult. Therefore, utilizing a small set of data with verified labels and massive data with noise labels to build a multi-label classification model becomes valuable for practical applications. In this paper, we propose a teacher-student network with non-linear feature transformation, leveraging massive dataset with noisy labels and a small dataset with verified data to learn a multi-label classifier. We use a non-linear feature transformation to map the feature space and the label space. We first pre-train both the teacher and student networks with noisy labels and then train both networks jointly. We build a multi-label dataset based on MS COCO2014 for performance evaluation, in which both noisy label and verified label are given for each image. Experimental results on our dataset and the public-domain multi-label dataset (OpenImage) show that the proposed approach is effective in leveraging massive noisy labels to build multi-label classifiers.