## 4 LSUN-Stanford Car Dataset  
It was discovered in Section 2 that existing large car datasets do not meet the requirements for GAN training fully, especially in terms of image zoom level and pose. To the best of our knowledge, the most appropriate datasets for training GANs are currently the LSUN car dataset due to its large size, and the Stanford car dataset due to its unambiguousness. The LSUN car dataset consists of 5,520,753 car images but has many flaws in terms of noise and image accuracy (e.g., on some images there is not a single car, but rather some trucks, people, vans, etc.). Some of these problems are demonstrated in Figure 1. The Stanford car dataset consists of 16,185 car images which are much more accurate and have less noise, but the size of this dataset is inadequate for GAN training. Due \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Discriminator** & **Activation** & **Output Shape** & **Params** \\ \hline Input image & – & \(3\times 1024\times 1024\) & – \\ Conv \(1\times 1\) & LReLU & \(16\times 1024\times 1024\) & 64 \\ Conv \(3\times 3\) & LReLU & \(16\times 1024\times 1024\) & 2.3k \\ Conv \(3\times 3\) & LReLU & \(32\times 1024\times 1024\) & 4.6k \\ Downsample & – & \(32\times 512\times 512\) & – \\ \hline Conv \(3\times 3\) & LReLU & \(32\times 512\times 512\) & 9.2k \\ Conv \(3\times 3\) & LReLU & \(64\times 512\times 512\) & 18k \\ Downsample & – & \(64\times 256\times 256\) & – \\ \hline Conv \(3\times 3\) & LReLU & \(64\times 256\times 256\) & 37k \\ Conv \(3\times 3\) & LReLU & \(128\times 256\times 256\) & 74k \\ Downsample & – & \(128\times 128\times 128\) & – \\ \hline Conv \(3\times 3\) & LReLU & \(128\times 128\times 128\) & 148k \\ Conv \(3\times 3\) & LReLU & \(256\times 128\times 128\) & 295k \\ Downsample & – & \(256\times 64\times 64\) & – \\ \hline Conv \(3\times 3\) & LReLU & \(256\times 64\times 64\) & 590k \\ Conv \(3\times 3\) & LReLU & \(512\times 64\times 64\) & 1.2M \\ Downsample & – & \(512\times 32\times 32\) & – \\ \hline Conv \(3\times 3\) & LReLU & \(512\times 32\times 32\) & 2.4M \\ Conv \(3\times 3\) & LReLU & \(512\times 32\times 32\) & 2.4M \\ Downsample & – & \(512\times 16\times 16\) & – \\ \hline Conv \(3\times 3\) & LReLU & \(512\times 16\times 16\) & 2.4M \\ Conv \(3\times 3\) & LReLU & \(512\times 16\times 16\) & 2.4M \\ Downsample & – & \(512\times 8\times 8\) & – \\ \hline Conv \(3\times 3\) & LReLU & \(512\times 8\times 8\) & 2.4M \\ Conv \(3\times 3\) & LReLU & \(512\times 8\times 8\) & 2.4M \\ Downsample & – & \(512\times 4\times 4\) & – \\ \hline Minibatch stddev & – & \(513\times 4\times 4\) & – \\ Conv \(3\times 3\) & LReLU & \(512\times 4\times 4\) & 2.4M \\ Conv \(4\times 4\) & LReLU & \(512\times 1\times 1\) & 4.2M \\ Fully-connected & linear & \(1\times 1\times 1\) & 513 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Discriminator architecture of the StyleGAN neural network [2]. to the already large number of images available in the LSUN [4] and Stanford [7] car datasets, it was decided in this research to couple and prune both datasets in such a way that a merged dataset would be more suitable for the above-mentioned GAN needs. Serious shortcomings of both datasets are that images are often taken from online car adverts and unreliable pages, have poor backgrounds, and have the cars overlapping with other objects in the scene. All this restricts the GANs from being trained perfectly. A small amount of noise in the dataset is useful as it reduces the possibility of overfitting. However, a greater amount of noise simply reduces the training quality [20,21]. Some problematic images from the LSUN and the Stanford car datasets are depicted in Figures 1 and 2. In the sequel of this research, we took images from the LSUN [4] and the Stanford car [7] datasets and joined them in one single dataset. The total number of images after merging both datasets was 5,536,938. Subsequently, pre-trained neural networks and deep learning methods were used to prune and annotate this new dataset.