创意肖像创作：探索创意对抗网络与条件创意对抗网络
Creative Portraiture: Exploring Creative Adversarial Networks and Conditional Creative Adversarial Networks
AI总结：
本文研究DCGAN创意生成局限，探索CAN并提出CCAN，利用WikiArt生成新颖肖像，并支持按风格标签条件化生成，以更贴近人类创作过程。
摘要：
卷积神经网络（CNNs）已与生成对抗网络（GANs）相结合，形成了深度卷积生成对抗网络（DCGANs），并取得了巨大成功。DCGANs已被用于从时装设计和绘画等创意领域生成图像和视频。在创意应用中使用DCGANs的一个常见批评是，它们在生成创意产品方面的能力有限，因为生成器只是学习复制训练分布。我们探索了DCGANs的一种扩展，即创意对抗网络（CANs）。利用CANs，我们使用WikiArt数据集训练网络，生成新颖、富有创意的肖像。此外，我们介绍了CANs的扩展——条件创意对抗网络（CCANs），并展示了其根据风格标签生成创意肖像的潜力。我们认为，生成以风格标签为条件或受其启发的作品，能够紧密模拟真实的创作过程：在这些过程中，人类创作出仍植根于既有风格的富有想象力的作品。
英文摘要：
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.
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