通过质量多样性探索GANs的进化
Exploring the Evolution of GANs through Quality Diversity
摘要：
生成对抗网络（GANs）在生成算法领域取得了相关进展，主要在图像背景下呈现高质量的结果。然而，GANs很难训练，模型的几个方面应该事先手工设计，以确保训练成功。在这种背景下，提出了诸如COEGAN的进化算法来解决GAN训练中的挑战。然而，在这些解决方案中，可以发现缺乏多样性和过早优化。本文提出了一种质量多样性算法在GANs进化中的应用。该解决方案是基于新颖性搜索与本地竞争（NSLC）算法，适应COEGAN中使用的概念，这一新的建议。我们比较我们的建议与原来的COEGAN模型和一个替代版本，使用全球竞争的方法。实验结果表明，我们的建议增加了发现的解决方案的多样性，并利用该算法发现的模型的性能。此外，全球竞争方法能够始终如一地为GANs找到更好的模型。
英文摘要：
Generative adversarial networks (GANs) achieved relevant advances in the field of generative algorithms, presenting high-quality results mainly in the context of images. However, GANs are hard to train, and several aspects of the model should be previously designed by hand to ensure training success. In this context, evolutionary algorithms such as COEGAN were proposed to solve the challenges in GAN training. Nevertheless, the lack of diversity and premature optimization can be found in some of these solutions. We propose in this paper the application of a quality-diversity algorithm in the evolution of GANs. The solution is based on the Novelty Search with Local Competition (NSLC) algorithm, adapting the concepts used in COEGAN to this new proposal. We compare our proposal with the original COEGAN model and with an alternative version using a global competition approach. The experimental results evidenced that our proposal increases the diversity of the discovered solutions and leverage the performance of the models found by the algorithm. Furthermore, the global competition approach was able to consistently find better models for GANs.
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。
