以技能等级为适应度的GANs进化
Using Skill Rating as Fitness on the Evolution of GANs
摘要：
生成对抗网络（GANs）是一种对抗模型，在生成任务上取得了令人印象深刻的结果。尽管有相关的结果，但GANs在稳定性方面存在一些挑战，使得训练通常是一个偶然的过程。为了克服这些挑战，提出了一些改进，以更好地处理模型的内部特征，例如替代损失函数或发生器和神经网络使用的神经网络的架构变化。最近的工作提出了在GAN训练中使用进化算法，旨在解决这些挑战，并提供一种自动找到好模型的方法。在这种情况下，COEGAN提出使用共同进化和神经进化来协调GANs的训练。然而，以前的实验发现，一些用于指导进化的适应度函数并不理想。在这项工作中，我们提出了一个基于游戏的适应度函数的评价内使用的COEGAN方法。技能等级是量化玩家在游戏中技能的指标，已经被用于评估GANs。我们扩展了这个想法，使用进化算法中的技能评级来训练GANs。结果表明，技能等级可以作为适应度来指导COEGAN的进化，而不依赖于外部评估器。
英文摘要：
Generative Adversarial Networks (GANs) are an adversarial model that achieved impressive results on generative tasks. In spite of the relevant results, GANs present some challenges regarding stability, making the training usually a hit-and-miss process. To overcome these challenges, several improvements were proposed to better handle the internal characteristics of the model, such as alternative loss functions or architectural changes on the neural networks used by the generator and the discriminator. Recent works proposed the use of evolutionary algorithms on GAN training, aiming to solve these challenges and to provide an automatic way to find good models. In this context, COEGAN proposes the use of coevolution and neuroevolution to orchestrate the training of GANs. However, previous experiments detected that some of the fitness functions used to guide the evolution are not ideal. In this work we propose the evaluation of a game-based fitness function to be used within the COEGAN method. Skill rating is a metric to quantify the skill of players in a game and has already been used to evaluate GANs. We extend this idea using the skill rating in an evolutionary algorithm to train GANs. The results show that skill rating can be used as fitness to guide the evolution in COEGAN without the dependence of an external evaluator.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
