## 5 Evaluating the GAN Performance for Data Augmentation  
To visualize the distribution of synthetic (augmented) and original datasets in a lower-dimensional space, we employed t-Distributed Stochastic Neighbor Embedding (t-SNE), a dimensionality reduction technique. t-SNE maps high-dimensional data to two dimensions, effectively preserving local relationships between points and revealing structure at multiple scales. Figure 6 presents t-SNE visualizations for the four GAN variants (GAN, WGAN, DCGAN, TSAGAN) at their final training epoch (2000), as well as the GAN at epoch 1 and noise-based augmentation for comparison. Original data points are depicted in blue and synthetic data points in red, providing a clear visual distinction.  
Figure 6: The t-SNE visualization of synthetic and original datasets (**a**) The training epoch of 1 for GAN (**b**) The training epoch of 2000 for GAN (**c**) The training epoch of 2000 for WGAN (**d**) The training epoch of 2000 for DCGAN (**e**) The training epoch of 2000 for TSAGAN (**f**) Augmentation via addition of noise.  
In the early stages of GAN training, the augmented data points are sparsely distributed and show little overlap with the original data, indicating poor alignment and high divergence. By epoch 2000, the GAN-augmented data points are well-integrated with the original data, demonstrating the GAN's capability to generate synthetic data that closely resembles the original dataset. The WGAN shows a unique pattern where augmented data forms distinct clusters that encompass the original data points, suggesting it captures the overall distribution well but may over-segment the data space. The DCGAN (Figure 6d) and TSAGAN (Figure 6e) both show good integration of augmented and original data, with the TSAGAN appearing to have a slightly more uniform distribution. In the last image, noise-based augmentation produces a distinct pattern where augmented data form concentric circles around original data points, indicating a simple additive noise approach that does not capture the underlying data distribution as effectively as GAN-based methods. These visualizations highlight the effectiveness of GAN-based augmentation techniques in generating high-quality synthetic data that closely mimics the original dataset. The increasing overlap and similarity in distribution between original and augmented data points across different GAN architectures demonstrate their capability to produce diverse yet representative samples, providing a robust foundation for deep learning models and ensuring balanced representation across all labels.  
The Wasserstein distance between the original and synthetic datasets across different epochs for the four GAN variants are illustrated in Figure 7, serving as a metric to quantify the similarity between the two distributions. Lower values indicate higher similarity. The WGAN shows the most rapid convergence, achieving the lowest Wasserstein distance of about 2.5 by epoch 100 and maintaining this level throughout training. The DCGAN and TSAGAN demonstrate similar convergence patterns, starting with high distances but steadily decreasing to around three by epoch 2000. The standard GAN, interestingly, shows the most volatile behavior, with an initial decrease followed by a spike at epoch 500, before eventually converging to a distance similar to the DCGAN and TSAGAN by epoch 2000. This comparison reveals that while all GAN variants eventually achieve similar levels of data similarity, they differ significantly in their convergence paths. The WGAN's rapid and stable convergence suggests it may be the most efficient in generating high-quality synthetic data, despite its underperformance in the final localization task. The standard GAN's volatility indicates a need for careful monitoring during training, although it ultimately achieves competitive results. Overall, the results confirm that with adequate training, the GAN-based augmentation technique significantly enhances the dataset, providing a balanced and high-quality training set for deep learning models.