## 2 Similar Work and Existing Car Datasets  
At this moment there are only a few specific datasets that exceed one million images, such as LSUN, Google Open Images, Tencent ML-Images, and ImageNet [7; 9; 10; 11]. The majority of them are created for image classification and segmentation tasks. There are only a few datasets that meet the specific requirements for successful GAN training. Let us list the three main requirements, namely, low intrinsic variation in (I) poses, (II) zoom levels, and (III) backgrounds [1]. [PERSON] et al. [1] have created the Flickr-Faces-HQ (FFHQ) dataset that contains 70,000 images of human faces, with a larger variation than the CelebA-HQ dataset [2] in terms of age, ethnicity, and background; and with better coverage of accessories, such as glasses, sunglasses, hats, and similar objects. All this was intended  
Figure 1: LSUN car dataset image noise examples [4].  
specifically for the StyleGAN training, due to the possibility to add some specific style or feature (e.g., glasses) to the network. However, using the CelebA-HQ dataset for training pointed out that the preparation of a specific dataset in terms of low intrinsic variation in poses, zoom levels, and background yields better results in GAN training. CelebA-HQ offers much higher quality and covers a considerably wider variation than the existing high-resolution datasets [1].  
The most popular datasets that include vehicles are KITTI [12], Stanford car dataset [7], Vehicle-1M [13], and the LSUN car dataset [4]. The KITTI object detection dataset contains 12,000 images of scenes with around 80,000 objects in total. The dataset was intended primarily for developing autonomous driving algorithms. Due to the high occlusion rate, it is not well suited for GAN training [12]. The Vehicle-1M dataset was created primarily for vehicle identification. It consists of 936,051 images taken from different traffic cameras in China, yet only 55,527 different vehicles are present, rendering it unsuitable for GAN training. Except for lack of vehicle diversity, the image quality is poor and of low resolution [13], and therefore inadequate for GAN training. Due to a large number of images, the LSUN car is one of the most popular datasets for GAN training. It suffers from high intrinsic variations in zoom levels, poses, and backgrounds of images. Additionally, the images can contain multiple cars; cars can also be occluded. Sample images from this dataset are depicted in Figure 1. The Stanford car dataset contains 16,185 images of 196 classes of cars. Each car class typically contains information about the make, model, and year [7]. It is much more pruned than the LSUN car dataset, but it is still affected by multiple car instances and noise in terms of watermarks on images, which can be seen in Figure 2.  
[PERSON] et al. [1] have managed to generate images with better precision and quality using the original StyleGAN architecture and FFHQ dataset. However, the generated results were not representative when the original StyleGAN was trained with the LSUN car dataset [1]. This observation suggests that the pruned LSUN car dataset, coupled with pruned Stanford car dataset, could improve GAN neural network training.