Interactive Multi-Label CNN Learning With Partial Labels

We address the problem of efficient end-to-end learning a multi-label Convolutional Neural Network (CNN) on training images with partial labels. Training a CNN with partial labels, hence a small number of images for every label, using the standard cross-entropy loss is prone to overfitting and performance drop. We introduce a new loss function that regularizes the cross-entropy loss with a cost function that measures the smoothness of labels and features of images on the data manifold. Given that optimizing the new loss function over the CNN parameters requires learning similarities among labels and images, which itself depends on knowing the parameters of the CNN, we develop an efficient interactive learning framework in which the two steps of similarity learning and CNN training interact and improve the performance of each another. Our method learns the CNN parameters without requiring keeping all training data in the memory, allows to learn few informative similarities only for images in each mini-batch and handles changing feature representations. By extensive experiments on Open Images, CUB and MS-COCO datasets, we demonstrate the effectiveness of our method. In particular, on the large-scale Open Images dataset, we improve the state of the art by 1.02% in mAP score over 5,000 classes.