Description: The term ‘Non-Stationary’ refers to processes where statistical properties change over time. In the context of Generative Adversarial Networks (GANs), this implies that the input data distribution may vary over time, complicating the training process of the network. GANs are deep learning models consisting of two neural networks: a generator and a discriminator, which compete against each other. When working with non-stationary data, the generator must continuously adapt to new data distributions, while the discriminator must also adjust to distinguish between real and generated data that may change over time. This dynamic can lead to significant challenges in model convergence and the quality of generated samples. Non-stationarity can arise in various applications, such as image generation, where visual trends may evolve, or in text synthesis, where style and content may vary over time. Therefore, understanding and managing non-stationarity is crucial for improving the robustness and effectiveness of GANs in diverse and dynamic environments.
