Tejeda-Ocampo Carlos, López-Cuevas Armando, Terashima-Marin Hugo
School of Engineering and Sciences, Tecnologico de Monterrey, 64849 Monterrey, Mexico.
Entropy (Basel). 2020 Dec 24;23(1):11. doi: 10.3390/e23010011.
Deep interactive evolution (DeepIE) combines the capacity of interactive evolutionary computation (IEC) to capture a user's preference with the domain-specific robustness of a trained generative adversarial network (GAN) generator, allowing the user to control the GAN output through evolutionary exploration of the latent space. However, the traditional GAN latent space presents feature entanglement, which limits the practicability of possible applications of DeepIE. In this paper, we implement DeepIE within a style-based generator from a StyleGAN model trained on the WikiArt dataset and propose StyleIE, a variation of DeepIE that takes advantage of the secondary disentangled latent space in the style-based generator. We performed two AB/BA crossover user tests that compared the performance of DeepIE against StyleIE for art generation. Self-rated evaluations of the performance were collected through a questionnaire. Findings from the tests suggest that StyleIE and DeepIE perform equally in tasks with open-ended goals with relaxed constraints, but StyleIE performs better in close-ended and more constrained tasks.
深度交互式进化（DeepIE）将交互式进化计算（IEC）捕捉用户偏好的能力与训练有素的生成对抗网络（GAN）生成器的特定领域鲁棒性相结合，允许用户通过对潜在空间的进化探索来控制GAN输出。然而，传统的GAN潜在空间存在特征纠缠问题，这限制了DeepIE可能应用的实用性。在本文中，我们在基于WikiArt数据集训练的StyleGAN模型的基于风格的生成器中实现了DeepIE，并提出了StyleIE，这是DeepIE的一种变体，它利用了基于风格的生成器中的二次解缠潜在空间。我们进行了两次AB/BA交叉用户测试，比较了DeepIE和StyleIE在艺术生成方面的性能。通过问卷调查收集了对性能的自评评估。测试结果表明，在约束宽松的开放式目标任务中，StyleIE和DeepIE表现相当，但在封闭式和约束更强的任务中，StyleIE表现更好。
