用创意对抗网络生成独特肖像画，让AI创作更贴近人类灵感。
Creative Portraiture: Exploring Creative Adversarial Networks and Conditional Creative Adversarial Networks
- 引入创意对抗网络，突破传统GAN复制训练数据的局限。
- 基于WikiArt数据集生成新颖肖像，实现风格化艺术创作。
- 提出条件创意对抗网络，可按风格标签生成定制化作品。
卷积神经网络（CNN）与生成对抗网络（GAN）结合形成深度卷积生成对抗网络（DCGAN），在时尚设计、绘画等创意领域成功生成图像与视频。但主流应用常被批评为仅复制训练数据分布，缺乏真正创造力。本文探索了DCGAN的扩展——创意对抗网络（CAN），利用WikiArt数据集训练模型，生成具有原创性的肖像作品。进一步提出条件创意对抗网络（CCAN），可依据风格标签生成受特定艺术风格启发的肖像，使生成过程更贴近人类创作中‘在传统基础上创新’的真实逻辑。
原文摘要 · Abstract (English)
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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