# Perturbative GAN: GAN with Perturbation Layers

Source: [Perturbative GAN: GAN with Perturbation Layers](<https://www.sota2.com/research/paper/68fa65561e209131cea09d2f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:24:52.728Z

Authors: Yuma Kishi, Tsutomu Ikegami, Shin-ichi O'uchi, Ryousei Takano, Wakana Nogami, Tomohiro Kudoh

Published: 2019-02-05T01:43:35.000Z

## Abstract

Perturbative GAN, which replaces convolution layers of existing convolutional GANs (DCGAN, WGAN-GP, BIGGAN, etc.) with perturbation layers that adds a fixed noise mask, is proposed. Compared with the convolu-tional GANs, the number of parameters to be trained is smaller, the convergence of training is faster, the incep-tion score of generated images is higher, and the overall training cost is reduced. Algorithmic generation of the noise masks is also proposed, with which the training, as well as the generation, can be boosted with hardware acceleration. Perturbative GAN is evaluated using con-ventional datasets (CIFAR10, LSUN, ImageNet), both in the cases when a perturbation layer is adopted only for Generators and when it is introduced to both Generator and Discriminator.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1902.01514>)

[PDF](<https://arxiv.org/pdf/1902.01514.pdf>)