Description: The term ‘Non-ideal’ in the context of Generative Adversarial Networks (GANs) refers to scenarios or conditions that do not meet the optimal or expected standards in the functioning of these models. GANs are a type of deep learning architecture consisting of two neural networks: a generator and a discriminator, which compete against each other. In ‘Non-ideal’ situations, the generator may produce results that are of low quality, unrealistic, or do not resemble the training data, which can be due to various factors such as an insufficient dataset, inadequate training, or lack of diversity in the data. These conditions can lead to the model not generalizing well, resulting in outputs that do not meet quality or relevance expectations. Identifying these scenarios is crucial for improving GAN performance, as it allows researchers and developers to adjust model parameters, optimize the dataset, and refine training techniques. In summary, ‘Non-ideal’ highlights the importance of optimal conditions in the development and implementation of generative networks, emphasizing that the quality of generated results largely depends on the quality of the data and the training process.
