Abstract
Generative adversarial network (GAN) is a powerful method to reproduce the distribution of a given data set. It is widely used for generating photo-realistic images or data collections that appear real. Evolutionary GAN (E-GAN) is one of state-of-the-art GAN variations. E-GAN combines population based search and evolutionary operators from evolutionary algorithms with GAN to enhance diversity and search performance. In this study we aim to improve E-GAN by adding transfer learning and crossover which is a key evolutionary operator that is commonly used in evolutionary algorithms, but not in E-GAN.
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Liu, Z., Sabar, N., Song, A. (2022). Improving Evolutionary Generative Adversarial Networks. In: Long, G., Yu, X., Wang, S. (eds) AI 2021: Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science(), vol 13151. Springer, Cham. https://doi.org/10.1007/978-3-030-97546-3_56
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