论文标题
DC-ART-GAN：使用DC-GAN用于数字艺术的稳定程序内容生成
DC-Art-GAN: Stable Procedural Content Generation using DC-GANs for Digital Art
论文作者
论文摘要
艺术是一种使用数字技术作为生成或创造过程的一部分的艺术方法。随着数字货币和NFT（不可杀死的代币）的出现，对数字艺术的需求正在积极增长。在本手稿中，我们主张将深层生成网络和对抗性培训进行稳定而变体的艺术生成的概念。这项工作主要集中于使用深卷积生成对抗网络（DC-GAN），并探讨了解决GAN训练中常见陷阱的技术。我们比较了DC-GAN的各种架构和设计，以为稳定而逼真的一代提供推荐的设计选择。这项工作的主要重点是生成现实中不存在的逼真的图像，而是通过提出的模型从随机噪声中合成的。我们为生成的动物面部图像（一些证据表明物种混合物）以及训练，建筑和设计选择的建议提供视觉结果。我们还展示了训练图像预处理如何在GAN培训中起着重要作用。
Art is an artistic method of using digital technologies as a part of the generative or creative process. With the advent of digital currency and NFTs (Non-Fungible Token), the demand for digital art is growing aggressively. In this manuscript, we advocate the concept of using deep generative networks with adversarial training for a stable and variant art generation. The work mainly focuses on using the Deep Convolutional Generative Adversarial Network (DC-GAN) and explores the techniques to address the common pitfalls in GAN training. We compare various architectures and designs of DC-GANs to arrive at a recommendable design choice for a stable and realistic generation. The main focus of the work is to generate realistic images that do not exist in reality but are synthesised from random noise by the proposed model. We provide visual results of generated animal face images (some pieces of evidence showing a blend of species) along with recommendations for training, architecture and design choices. We also show how training image preprocessing plays a massive role in GAN training.
