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Tarun Kulshrestha

2 accepted papers

2024

RG-GAN: Dynamic Regenerative Pruning for Data-Efficient Generative Adversarial Networks

AAAI 2024technical

Training Generative Adversarial Networks (GAN) to generate high-quality images typically requires large datasets. Network pruning during training has recently emerged as a significant advancement for data-efficient GAN. However, simple and straightforward pruning can lead to the risk of losing key i…

2023

Re-GAN: Data-Efficient GANs Training via Architectural Reconfiguration

CVPR 2023poster

Training Generative Adversarial Networks (GANs) on high-fidelity images usually requires a vast number of training images. Recent research on GAN tickets reveals that dense GANs models contain sparse sub-networks or "lottery tickets" that, when trained separately, yield better results under limited…