Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion. While they were successfully applied to many problems, training a GAN is a notoriously challenging task and requires a significant number of hyperparameter tuning, neural architecture engineering, and a non-trivial amount of "tricks". The success in many practical applications coupled with the lack of a measure to quantify the failure modes of GANs resulted in a plethora of proposed losses, regularization and normalization schemes, as well as neural architectures. In this work we take a sober view of the current state of GANs from a practical perspective. We discuss and evaluate common pitfalls and reproducibility issues, open-source our code on Github, and provide pre-trained models on TensorFlow Hub.
翻译:产生对抗性网络(GANs)是一组深层次的基因模型,旨在以不受监督的方式学习目标分布;虽然这些模型成功地应用于许多问题,但培训GAN是一项臭名昭著的挑战性任务,需要大量的超参数调节、神经结构工程和非三轨数量的“tricks ” 。 许多实际应用的成功,加上缺乏量化GANs失败模式的措施,导致了大量拟议的损失、正规化和正常化计划以及神经结构。 在这项工作中,我们从实际角度清醒地看待GANs的现状。 我们讨论和评估共同的陷阱和复制问题,开源于Github的代码,并提供TensorFlow HUb的预培训模型。