# TextControlGAN: Text-to-Image Synthesis with Controllable Generative Adversarial Networks
## 1 Introduction  
The rapid advancement of artificial intelligence (AI) technologies, particularly in the domains of machine learning and deep learning, has fostered the development of various AI models [1, 2, 3]. Generative models utilizing AI frameworks have garnered significant attention, as they learn from provided sample distributions and create samples that closely mimic the features of the training data [4, 5, 6, 7]. These models have been successfully applied to numerous image processing and data analysis tasks due to their ability to generate interesting and realistic samples without requiring the learning of complex structural features [8, 9, 10]. Generative adversarial networks (GANs) are a prominent type of generative model, capable of producing realistic samples by learning the latent space of a dataset [11, 12, 13, 14]. A GAN consists of two neural networks, namely the generator and the discriminator. The generator receives a random noise vector as input and aims to create fake samples that closely resemble real samples. The discriminator, on the other hand, learns to differentiate between real samples and fake samples generated by the generator. Through an iterative process of deception and detection, the generator and discriminator improve their performance, ultimately synthesizing a generated sample distribution that minimizes the difference from the real sample distribution. In GAN, all data are considered random variables with their corresponding probability distributions. Each time a random variable is measured, it produces a different value, making it necessary to understand the probability distribution of the random variable to generate numbers that adhere to a specific distribution. By having knowledge of the probability distribution, the statistical properties of the data can be analyzed. GAN generates data randomly to conform to a given probability distribution, resulting in generated data with values comparable to the original data used to determine the probability distribution. Thus, the ultimate goal of GAN is to estimate the probability distribution of unprocessed data, enabling an artificial neural network to generate an infinite number of new datasets that share the exact same probability distribution as the original data. In summary, GAN learns the probability distribution and uses deep learning to generate this distribution. GANs have been expanded in various domains [6; 15; 16], including image and video processing as well as image translation, voice signals, 3D rendering, and natural language processing [17; 18; 19; 20; 21; 22; 23; 24]. One notable application is text-to-image synthesis, which involves generating synthetic images based on given conditional text inputs [25; 26; 27; 28; 29]. By using different input noise vectors, GANs can generate distinct synthetic images corresponding to the same input sentence. However, conventional GANs face a significant limitation in text-to-image synthesis tasks: due to the use of a random distribution as the input noise vector, controlling the features of the generated samples based on input texts is challenging [30]. To overcome this limitation, conditional GANs (cGANs) have been introduced, allowing for the generation of text-conditional images by incorporating text-conditional encoding vectors in the generator and discriminator [31; 32; 33]. The cGAN-based GAN with conditional latent space interpolation and manifold interpolation (GAN-CLS-INT) has been proposed for text-to-image synthesis [25]. Although GAN-CLS-INT can generate natural-looking images from textual descriptions, it often fails to produce images that fully correspond to the given text, with generated images only partially reflecting the context of the input text [34; 35]. In this study, we introduce TextControlGAN, a groundbreaking text-to-image synthesis model that expands upon the ControlGAN architecture [36]. The primary innovation of ControlGAN lies in the inclusion of a more advanced classifier, as opposed to the classification component within the cGAN discriminator.