Inverse Convolutional Neural Networks for Learning from Label Proportions

Abstract

Learning from label proportions (LLP) is a new kind of learning problem which has attracted wide interest in the field of machine learning. Different from the well-known supervised learning, the training data of LLP is in form of bags and only the proportion of each class in each bag is available. Actually, many modern applications can be abstracted to this problem such as modeling voting behaviors and spam filtering. In this paper, we propose an end-to-end LLP model based on convolutional neural network called IDLLP, which employs the the idea of inverting a classifier calibration process to learn a classifier from bag probabilities. Firstly, convolutional neural network regression is used to estimate the values obtained by inverting the probability of each bag. Secondly, stochastic gradient descent based on batch is adapt to train the model, where the batch size depends on the bag size. At last, experiments demonstrate that our algorithm can obtain the best accuracies on image data compared with several recently developed methods.