Deep learning from label proportions with labeled samples

Learning from label proportions (LLP), where the training data is in form of bags, and only the proportions of classes in each bag are available, has attracted wide interest in machine learning community. In general, most LLP algorithms adopt random sampling to obtain the proportional information of different categories, which correspondingly obtains some labeled samples in each bag. However, LLP training process always fails to leverage these labeled samples, which may contain essential data distribution information. To address this issue, in this paper, we propose end-to-end LLP solver based on convolutional neural networks (ConvNets), called LLP with labeled samples (LLP-LS). First, we reshape the cross entropy loss in ConvNets, so that it can combine the proportional information and labeled samples in each bag. Second, in order to comply with the training data in a bag manner, ADAM based on batch is employed to train LLP-LS. Hence, the batch size in training process is in accordance with the bag size. Compared with up-to-date methods on multi-class problem, our algorithm can obtain the state-of-the-art on several image datasets.