What is the role of "data normalization" in the training of GANs?

The role of data normalization in the training of Generative Adversarial Networks (GANs) is to ensure consistent data ranges and improve model convergence. When training GANs, it's essential that the input data have a uniform distribution, typically rescaled to fit within a specific range, like [0, 1] or [-1, 1]. This uniformity allows the neural networks within the GAN to learn more effectively, as it prevents the model from being biased towards features that might be dominant in an unnormalized dataset.

When the data is normalized, it helps maintain a stable gradient during training, which is crucial for the effective optimization of deep learning models. This stability allows the generator and discriminator networks to learn concurrently and adapt in a balanced manner, promoting smoother convergence toward an optimal solution. Inconsistent data ranges could result in erratic learning behaviors, such as exploding or vanishing gradients, which can hinder the training process.

Conversely, aims such as maximizing diversity or reducing overfitting are relevant in their own contexts but are not the primary purpose of normalization in the GAN training process. Additionally, simplifying the data structure does not directly correlate with the main goal of normalization, which is primarily about consistency and convergence rather than altering the inherent complexity or structure