Improving Generative Adversarial Network Generalization for Facial Expression Synthesis
Facial expression synthesis aims to generate realistic facial expressions while preserving identity. Existing conditional generative adversarial networks (GANs) achieve excellent image-to-image translation results, but their performance often degrades when test images differ from the training dataset. We present Regression GAN (RegGAN), a model that learns an intermediate representation to improve generalization beyond the training distribution. RegGAN consists of two components: a regression layer with local receptive fields that learns expression details by minimizing the reconstruction error through a ridge regression loss, and a refinement network trained adversarially to enhance the ...
| Authors | Arbish Akram, Nazar Khan, Arif Mahmood |
|---|---|
| Primary category | cs.CV |
| Published | 4 March 2026 |
| Last updated at source | 4 March 2026 |
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