rGAN | rGAN : Label-Noise Robust Generative Adversarial Networks | Machine Learning library

rGAN is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Generative adversarial networks applications. rGAN has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Our task is, when given noisy labeled images, to construct a label-noise robust conditional generator that can generate an image conditioned on the clean label rather than conditioned on the noisy label. Our main idea for solving this problem is to incorporate a noise transition model (viewed as orange rectangles in (b) and (d); which represents a probability that a clean label is corrupted to a noisy label) into typical class conditional GANs. In particular, we develop two variants: rAC-GAN (b) and rcGAN (d) that are extensions of AC-GAN [1] (a) and cGAN [2, 3] (c), respectively.