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This study performs an ablation analysis of Vector Quantized GenerativeAdversarial Networks (VQGANs), concentrating on image-to-image synthesisutilizing a single NVIDIA A100 GPU. The current work explores the nuancedeffects of varying critical parameters including the number of epochs, imagecount, and attributes of codebook vectors and latent dimensions, specificallywithin the constraint of limited resources. Notably, our focus is pinpointed onthe vector quantization loss, keeping other hyperparameters and loss components(GAN loss) fixed. This was done to delve into a deeper understanding of thediscrete latent space, and to explore how varying its size affects thereconstruction. Though, our results do not surpass the existing benchmarks,however, our findings shed significant light on VQGAN's behaviour for a smallerdataset, particularly concerning artifacts, codebook size optimization, andcomparative analysis with Principal Component Analysis (PCA). The study alsouncovers the promising direction by introducing 2D positional encodings,revealing a marked reduction in artifacts and insights into balancing clarityand overfitting.

Vector quantization loss analysis in VQGANs: a single-GPU ablation study for image-to-image synthesis