We propose a novel method for generating abstract art. First an autoencoder is trained to encode and decode the style representations of images, which are extracted from source images with a pretrained VGG network. Then, the decoder component of the autoencoder is extracted and used as a generator in a GAN. The generator works with an ensemble of discriminators. Each discriminator takes different style representations of the same images, and the generator is trained to create images that create convincing style representations in order to deceive all of the generators. The generator is also trained to maximize a diversity term. The resulting images had a surreal, geometric quality. We call our approach ARTEMIS (ARTistic Encoder- Multi- Discriminators Including Self-Attention), as it uses the self-attention layers and an encoder-decoder architecture.

翻译：我们提出了一种生成抽象艺术的新方法。首先训练一个自编码器，用于编码和解码从源图像中通过预训练的VGG网络提取的风格表征。随后，提取自编码器的解码器部分作为生成对抗网络中的生成器。该生成器与一组判别器协同工作：每个判别器处理同一图像的不同风格表征，生成器通过训练生成图像以产生令人信服的风格表征，从而欺骗所有判别器。同时，生成器还通过训练最大化多样性项。最终生成的图像具有超现实几何质感。我们将该方法命名为ARTEMIS（基于自注意力机制的多判别器艺术编码器），因其采用了自注意力层与编码器-解码器架构。