Abstract
Convolutional neural networks (CNNs) have been combined with generative adversarial networks (GANs) to create deep convolutional generative adversarial networks (DCGANs) with great success. DCGANs have been used for generating images and videos from creative domains such as fashion design and painting. A common critique of the use of DCGANs in creative applications is that they are limited in their ability to generate creative products because the generator simply learns to copy the training distribution. We explore an extension of DCGANs, creative adversarial networks (CANs). Using CANs, we generate novel, creative portraits, using the WikiArt dataset to train the network. Moreover, we introduce our extension of CANs, conditional creative adversarial networks (CCANs), and demonstrate their potential to generate creative portraits conditioned on a style label. We argue that generating products that are conditioned, or inspired, on a style label closely emulates real creative processes in which humans produce imaginative work that is still rooted in previous styles.
Abstract (translated)
卷积神经网络（CNN）与生成对抗网络（GAN）的结合已成功创建了深度卷积生成对抗网络（DCGAN）。DCGAN已被用于从创意领域如时装设计和绘画中生成图像和视频。然而，对于在创意应用中使用DCGAN的一个常见批评是它们在生成创意产品方面的能力受限，因为生成器只是学习复制训练分布。我们探讨了DCGAN的一种扩展——创造性对抗网络（CAN），并利用WikiArt数据集训练网络来生成新颖的、具有创造性的肖像画。此外，我们介绍了CAN的进一步扩展——条件创造性对抗网络（CCAN），并通过示范其根据样式标签生成创意肖像的能力展示了它们的潜力。我们认为，基于或受某种风格标签启发而生成的产品能更贴近人类实际创作过程中产生富有想象力但仍植根于先前风格的作品的过程。
URL
https://arxiv.org/abs/2412.07091
