Neural architecture search (NAS) has witnessed prevailing success in image
classification and (very recently) segmentation tasks. In this paper, we
present the first preliminary study on introducing the NAS algorithm to
generative adversarial networks (GANs), dubbed AutoGAN. The marriage of NAS and
GANs faces its unique challenges. We define the search space for the generator
architectural variations and use an RNN controller to guide the search, with
parameter sharing and dynamic-resetting to accelerate the process. Inception
score is adopted as the reward, and a multi-level search strategy is introduced
to perform NAS in a progressive way. Experiments validate the effectiveness of
AutoGAN on the task of unconditional image generation. Specifically, our
discovered architectures achieve highly competitive performance compared to
current state-of-the-art hand-crafted GANs, e.g., setting new state-of-the-art
FID scores of 12.42 on CIFAR-10, and 31.01 on STL-10, respectively. We also
conclude with a discussion of the current limitations and future potential of
AutoGAN. The code is available at this https URL

本文介绍了一项有关 AutoGAN 的初步研究，这是将神经架构搜索算法引入生成对抗网络中的首个尝试。通过使用 RNN 控制器来引导搜索生成器构架的变化，实验结果验证了 AutoGAN 在无条件图像生成任务中的有效性，并与当前最先进的手工制作的 GANs 相比，取得了高竞争性的表现。

AutoGAN: 生成对抗网络的神经结构搜索

AutoGAN: Neural Architecture Search for Generative Adversarial Networks

To detect GAN generated images, conventional supervised machine learning
algorithms require collection of a number of real and fake images from the
targeted GAN model. However, the specific model used by the attacker is often
unavailable. To address this, we propose a GAN simulator, AutoGAN, which can
simulate the artifacts produced by the common pipeline shared by several
popular GAN models. Additionally, we identify a unique artifact caused by the
up-sampling component included in the common GAN pipeline. We show
theoretically such artifacts are manifested as replications of spectra in the
frequency domain and thus propose a classifier model based on the spectrum
input, rather than the pixel input. By using the simulated images to train a
spectrum based classifier, even without seeing the fake images produced by the
targeted GAN model during training, our approach achieves state-of-the-art
performances on detecting fake images generated by popular GAN models such as
CycleGAN.

本文提出了一个可以模拟多个流行 GAN 模型的 GAN 模拟器 AutoGAN，并基于频域的特性提出了一种分类器模型，从而即使在自主学习时无法看到目标 GAN 模型生成的假图像，也可以在检测流行 GAN 模型生成的假图像方面实现最先进的表现。