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 into a deeper understanding of the discrete latent space, and to explore how varying its size affects the reconstruction. Though, our results do not surpass the existing benchmarks, however, our findings shed significant light on VQGAN's behaviour for a smaller dataset, particularly concerning artifacts, codebook size optimization, and comparative analysis with Principal Component Analysis (PCA). The study also uncovers the promising direction by introducing 2D positional encodings, revealing a marked reduction in artifacts and insights into balancing clarity and overfitting.

翻译：本研究对向量量化生成对抗网络（VQGANs）进行了消融分析，重点聚焦于采用单张NVIDIA A100 GPU实现的图像到图像合成任务。当前工作探讨了在有限资源约束下，调整关键参数（包括训练轮数、图像数量、码本向量属性及潜在维度）所产生的细微影响。值得注意的是，我们将研究焦点明确锁定在向量量化损失上，同时保持其他超参数和损失分量（如GAN损失）固定不变。此举旨在深入理解离散潜在空间，并探究其规模变化对重建效果的影响机制。尽管实验结果未超越现有基准，但我们的发现对小数据集条件下VQGAN的行为特征提供了重要启示，尤其涉及伪影现象、码本尺寸优化以及主成分分析（PCA）的对比研究。此外，本研究通过引入二维位置编码揭示了富有前景的研究方向，显著减少了伪影现象，并为平衡清晰度与过拟合问题提供了重要见解。