  Generative Adversarial Networks (GANs) have extended deep learning to complex
generation and translation tasks across different data modalities. However,
GANs are notoriously difficult to train: Mode collapse and other instabilities
in the training process often degrade the quality of the generated results,
such as images. This paper presents a new technique called TaylorGAN for
improving GANs by discovering customized loss functions for each of its two
networks. The loss functions are parameterized as Taylor expansions and
optimized through multiobjective evolution. On an image-to-image translation
benchmark task, this approach qualitatively improves generated image quality
and quantitatively improves two independent GAN performance metrics. It
therefore forms a promising approach for applying GANs to more challenging
tasks in the future.
