Lightweight facial attribute editing with separable latent vector

Jintao Gu; Rui Li; Tingting Yang; Jiao Zhang

doi:10.1117/12.3010591

1 December 2023 Lightweight facial attribute editing with separable latent vector

Jintao Gu, Rui Li, Tingting Yang, Jiao Zhang

Proceedings Volume 12940, Third International Conference on Control and Intelligent Robotics (ICCIR 2023); 1294039 (2023) https://doi.org/10.1117/12.3010591
Event: Third International Conference on Control and Intelligent Robotics (ICCIR 2023), 2023, Sipsongpanna, China

Abstract

There are two main challenges faced by generative adversarial networks (GANs) for facial attribute editing. The first challenge is how to perform targeted attribute editing in a controllable manner during the editing process, preserving the variability of relevant attributes while ensuring the invariance of irrelevant attributes. The second challenge is the high computational resource requirements of GANs, making them demanding on hardware performance for almost all image editing GANs. To address these challenges, we propose our SLGAN model based on StyleGAN2, which incorporates separable loss and knowledge distillation methods. Experimental results demonstrate that our proposed model achieves promising performance on relevant tasks.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jintao Gu, Rui Li, Tingting Yang, and Jiao Zhang "Lightweight facial attribute editing with separable latent vector", Proc. SPIE 12940, Third International Conference on Control and Intelligent Robotics (ICCIR 2023), 1294039 (1 December 2023); https://doi.org/10.1117/12.3010591

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KEYWORDS

Performance modeling

Gallium nitride

Image quality

Wavelets

Data modeling

Mathematical optimization

Discrete wavelet transforms

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