Boundary Seeking GANs

no code implementations • ICLR 2018 • R. Devon Hjelm, Athul Paul Jacob, Adam Trischler, Gerry Che, Kyunghyun Cho, Yoshua Bengio

We introduce a method for training GANs with discrete data that uses the estimated difference measure from the discriminator to compute importance weights for generated samples, thus providing a policy gradient for training the generator.

Scene Understanding Text Generation