论文标题
通过质量多样性探索gan的演变
Exploring the Evolution of GANs through Quality Diversity
论文作者
论文摘要
生成对抗网络（GAN）在生成算法领域取得了相关的进步，主要在图像的上下文中呈现高质量的结果。但是，甘斯很难训练，并且该模型的几个方面应先前手工设计，以确保培训成功。在这种情况下，提出了诸如Coegan之类的进化算法来解决GAN训练中的挑战。然而，在其中一些解决方案中可以找到缺乏多样性和过早优化。我们在本文中提出了质量多样性算法在gan的发展中的应用。该解决方案基于与本地竞争（NSLC）算法的新颖性搜索，将Coegan中使用的概念调整为这一新建议。我们将我们的建议与原始的Coegan模型以及使用全球竞争方法的替代版本进行了比较。实验结果证明，我们的建议增加了发现的解决方案的多样性，并利用了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找到更好的gan模型。
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.
