  In this paper, we leverage generative adversarial networks (GANs) to derive
an effective algorithm LLP-GAN for learning from label proportions (LLP), where
only the bag-level proportional information in labels is available. Endowed
with end-to-end structure, LLP-GAN performs approximation in the light of an
adversarial learning mechanism, without imposing restricted assumptions on
distribution. Accordingly, we can directly induce the final instance-level
classifier upon the discriminator. Under mild assumptions, we give the explicit
generative representation and prove the global optimality for LLP-GAN.
Additionally, compared with existing methods, our work empowers LLP solver with
capable scalability inheriting from deep models. Several experiments on
benchmark datasets demonstrate vivid advantages of the proposed approach.
