Difference-Seeking Generative Adversarial Network
We propose a novel algorithm, Difference-Seeking Generative Adversarial Network, developed from traditional GAN.DSGAN considers the scenario that the training samples of target distribution,, are difficult to collect.Suppose there are two distributions and such that the density of the target distribution can be the differences between the densities of and.We show how to learn the target distribution only via samples from and.DSGAN has the flexibility to produce samples from various target distributions.Two key applications, semi-supervised learning and adversarial training, are taken as examples to validate the effectiveness of DSGAN.We also provide theoretical analyses about the convergence of DSGAN.
We proposed "Difference-Seeking Generative Adversarial Network" (DSGAN) model to learn the target distribution which is hard to collect training data.This paper presents DS-GAN, which aims to learn the difference between any two distributions whose samples are difficult or impossible to collect, and shows its effectiveness on semi-supervised learning and adversarial training tasks.This paper considers the problem of learning a GAN to capture a target distribution with only very few training samples from that distribution available.