## 4 Results and Discussion
### RQ3: What Evaluation Methods Are Used to Assess the Found Architecture?
#### 4.3.2 Datasets
Its massive training set contains 120,000 to 3,000,000 images per class, complemented by smaller validation and test sets for comprehensive model evaluation. While LSUN offers valuable resources for researchers, it presents challenges. Its size may strain computational resources, limiting accessibility. The significant imbalance between categories could introduce bias in trained models. Additionally, the 10 categories, while diverse, may not fully represent real-world complexity. [PERSON] et al. in E-GAN reported training and testing of the dataset, but the study lacks crucial quantitative evaluation, presenting only generated images without metric scores. The CIFAR-10 [54] dataset is a popular benchmark, evaluated extensively in recent works (e.g. [12; 14; 22]), It consist of 60,000 color images (32 \(\times\) 32 pixels) across 10 classes. It is split into 50,000 training images and 10,000 test images. The training set is divided into 5 batches of 10,000 images each. These batches are randomized, but collectively contain 5000 images per class. The single test batch has 1000 images from each class, ensuring balanced evaluation. This structure makes CIFAR-10 ideal for developing and testing image classification algorithms, especially for small-scale, multi-category tasks. In the context of NAS-GAN, CIFAR-10 serves as a valuable benchmark. When applied to CIFAR-10, NAS-GAN aims to generate high-quality, diverse images matching the dataset's 10 classes. The challenge lies in capturing intricate details within each category, given the small image size and diverse object types. CIFAR-10's manageable size and well-defined classes make it suitable for refining NAS-GAN techniques before tackling more complex image generation tasks. The results of the reviewed literature on this dataset are presented in Table 5, with EWSGAN currently achieving state-of-the-art performance. The STL-10 dataset is an advanced benchmark, examined in several studies (e.g., [12; 14; 29]). It isdesigned to improve upon the CIFAR-10 dataset. It consists of 100,000 color images with a higher resolution of 96 \(\times\) 96 pixels, spread across 10 classes. It is divided into three main components: a training set of 5000 labeled images (500 per class), a test set of 8000 images (800 per class), and a large pool of 100,000 unlabeled images. A key feature of STL-10 is its substantial unlabeled dataset of 100,000 images, drawn from a broader distribution than the labeled sets. All images are derived from labeled examples on ImageNet. The increased resolution of 96 \(\times\) 96 pixels presents a more challenging benchmark for scalable unsupervised learning methods compared to CIFAR-10. For NAS-GAN, STL-10 offers a more demanding benchmark than CIFAR-10. The higher resolution images and large unlabeled set allow exploration of more complex GAN architectures. The challenge is generating high-quality, diverse images that capture details of the 10 labeled classes while potentially leveraging the broader distribution in the unlabeled set. The results of the reviewed literature on these datasets are presented in Table 5, with EWSGAN currently achieving state-of-the-art performance.