Reviews: Learning from Label Proportions with Generative Adversarial Networks

Summary: The paper proposes to use GANs in the LLP setting, where only the proportions are known per bag of covariates (say images), the goal is to create a classifier on the covariate level. Similar to the previous work in semi-supervised learning with GANs, the discriminator in this case classifies between the current classes and a new class for fake samples. Here, the loss function like the normal GANs uses two term, one for the supervision for the matching of the label proportion, the other for the adversarial training. The authors then proceed to analyse this loss function. Originality: I have not previously seen the use of GANs in the LLP setting or the use of the lower bound approximation that allows for SGD on individuals.