Description: Imbalanced datasets refer to situations where samples from different classes are not uniformly distributed. This phenomenon is especially relevant in the context of machine learning models, including Generative Adversarial Networks (GANs), where the quality and diversity of training data are crucial for model performance. In an imbalanced dataset, one class may have significantly more samples than another, leading the model to learn biased patterns and not adequately represent the less frequent classes. This can result in data generation that favors majority classes, limiting the model’s ability to create realistic examples of minority classes. Identifying and managing imbalanced datasets is essential to ensure that GANs produce balanced and representative results. Techniques to address this issue include collecting more data for minority classes, using oversampling or undersampling techniques, and implementing penalty strategies during training to balance the influence of each class. In summary, imbalanced datasets are a significant challenge in training GANs, and proper handling is fundamental to the success of these networks in generating high-quality data.
