Deep Convolutional Generative Adversarial Networks (DCGAN)-Based Anime Face Generation

DOI

10.2991/978-94-6463-540-9_86How to use a DOI?

Keywords

Anime Face Generation; Deep Convolutional Generative Adversarial Networks (DCGAN); Training progress

Abstract

This study delves into the realm of anime face generation with the aim of empowering individuals to create their own anime characters and easing the burden on artists. Employing Deep Convolutional Generative Adversarial Networks (DCGAN), the research focuses on generating anime face images. The DCGAN model consists of a generator and a discriminator, each designed and trained for their respective roles. The generator employs a convolutional transpose structure, while the discriminator utilizes a convolutional neural network structure. Through simultaneous training of the generator and discriminator using a diverse dataset of anime face images, a comprehensive DCGAN model is developed. Leveraging the Kaggle dataset, the study evaluates the training progress of the model through loss change curves of the generator and discriminator, alongside the final generated anime face images. Comparing different loss change curves and generated images across varying epochs and batch sizes reveals superior performance with 60 epochs compared to 30 epochs, facilitating clearer facial features. Moreover, a batch size of 32 outperforms 256, attributed to its more stable loss change curve. These findings contribute valuable insights to the domain of anime face generation research.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Xunxiong Ou
PY  - 2024
DA  - 2024/10/16
TI  - Deep Convolutional Generative Adversarial Networks (DCGAN)-Based Anime Face Generation
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 863
EP  - 872
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_86
DO  - 10.2991/978-94-6463-540-9_86
ID  - Ou2024
ER  -