Lightweight Generative Adversarial Network (LGAN) Architectures for Resource-Constrained Hardware Applications

Generative Adversarial Networks (GANs) have achieved remarkable success in producing high-quality images. However, their deployment on resource-constrained devices, such as edge devices, is challenging due to heavy computational requirements and substantial memory usage. Despite recent advancements in compressing GANs, there is still room for further optimization, as existing methods may introduce potential model redundancies. To address this issue, we explore various optimization techniques, including Knowledge Distillation, Quantization-Aware Training, and Post-training quantization, aiming to create lightweight GANs that can generate high-fidelity images with reduced computational demands. Our experimental results on two benchmark datasets showcase significant compression achievements, with a $70.31 \%$ reduction in model size for Cycle GAN and a $\mathbf{7 5. 4 1 \%}$ reduction for Neural Style Transfer GAN, all while maintaining image quality.