Description: Manipulation in Generative Adversarial Networks (GANs) refers to the process of altering generated data to achieve desired characteristics or traits. This process is fundamental in training artificial intelligence models, where the goal is to optimize the quality and relevance of produced images, texts, or sounds. Manipulation may involve adjustments to model parameters, the selection of specific training data, or the modification of loss functions used to evaluate model performance. Through these techniques, researchers and developers can influence model behavior, guiding it toward generating results that meet specific quality or aesthetic criteria. Manipulation in this context not only focuses on improving the quality of generated data but can also be used to address bias issues in models, ensuring that outputs are fairer and more representative. In a world where artificial intelligence is increasingly present, manipulating generated data becomes a crucial tool for creating ethical and effective applications, allowing developers greater control over their model outcomes.
