## 4 Results and Discussion
### RQ2: What Are the Key Search Spaces Explored in NAS-GAN?
#### 4.2.2 Cell-Based Space  
Among the search spaces of NAS in GANs literature, the cell-based structure is the most used type, instead of searching for entire architectures, this method focuses on finding smaller architectural building blocks called cells or motifs. The final architecture is then created by stacking these cells in a predefined manner. It significantly reduces the size of the search space, as cells usually consist of fewer layers than complete architectures. This can lead to substantial speed-ups in the search process. When using a cell-based search space, a new design choice arises: how to choose the macro-architecture, i.e., how many cells to use and how to connect them to build the actual model. Lipizzaner [23] employs a cell-based search space, distributing Generators and Discriminators across a 2D toroidal grid. Each cell contains one or more GANs, interacting within defined neighborhoods (typically a center cell and its four adjacent cells). The search space encompasses neural network parameters, hyperparameters, and mixture weights. This structure allows for local adaptation and diversity maintenance, supporting various neural architectures (e.g., multi-layer perceptrons or deconvolutional GANs) within cells. Mustangs [24] adopts the search space to Lipizzaner. AutoGAN's [12] search space is designed to identify optimal Generator network architectures. It encompasses five key elements: skip connections, convolutional block varieties, normalization block options, upsampling techniques, and an in-cell shortcut indicator. The convolutional block category includes both pre- and post-activation variants, while the normalization block offers batch, instance, and no normalization choices. Upsampling options consist of bi-linear, nearest-neighbor, and stride-two deconvolution methods. To facilitate a direct comparison of search strategy efficacy and efficiency, E\({}^{2}\)GAN [36] and EWSGAN [31] adopt a search space identical to AutoGAN's. AGAN's [34] search space incorporates architectural design principles for both the Generator and Discriminator. The search methodology employs reinforcement learning, utilizing a controller comprised of a two-layer LSTM network. This controller navigates the search space to identify high-performing architectures within the predefined parameters. For upsampling, the system employs transposed convolution and nearest-neighbor interpolation techniques. The downsampling modules are constructed using two distinct atomic operations: one applies convolution before stride-2 average pooling, while the other reverses this order, performing stride-2 average pooling followed by convolution. AlphaGAN's [14] search space is composed of two main categories: normal operations and upsampling operations. Normal operations primarily consist of convolutional blocks. For upsampling, the framework employs three distinct methods: deconvolution, nearest-neighbor interpolation, and bi-linear interpolation. AdversarialNAS [39] introduced an extensive search space \(10^{3}\) for GANs, resulting in a continuous search domain. This approach employs probability distributions, with architecture representation defined by a set of continuous variables. The framework's search space is cell-based. For the Generator, it includes a variety of normal operations and upsampling techniques, such as bilinear interpolation, nearest-neighbor interpolation, and transposed convolution. The Discriminator's structure incorporates both normal operations and downsampling methods, including max pooling, average pooling, and convolutions. Thisexpansive and flexible search space allows for a comprehensive exploration of potential architectures, enhancing the adaptability and optimization of the GANs developed through this framework. EAGAN [30] implements the same search space to allow fair comparisons. EAS-GAN's [29] search space focuses on the Generator's architecture using a cell-based approach.