Vector quantization loss analysis in VQGANs: a single-GPU ablation study for image-to-image synthesis. (arXiv:2308.05242v1 [cs.CV])

This study performs an ablation analysis of Vector Quantized Generative
Adversarial Networks (VQGANs), concentrating on image-to-image synthesis
utilizing a single NVIDIA A100 GPU. The current work explores the nuanced
effects of varying critical parameters including the number of epochs, image
count, and attributes of codebook vectors and latent dimensions, specifically
within the constraint of limited resources. Notably, our focus is pinpointed on
the vector quantization loss, keeping other hyperparameters and loss components
(GAN loss) fixed. This was done to delve …

a100 a100 gpu analysis arxiv count current effects generative generative adversarial networks gpu image loss networks nvidia nvidia a100 quantization study synthesis vector work