Why is robustness important in GAN training?

Robustness in GAN training is crucial because it enables the model to effectively handle a variety of input conditions. When a GAN is robust, it means that it can maintain performance and generate high-quality outputs even in the presence of noise, variations in data distribution, or other unpredictable factors that may occur in real-world scenarios. This adaptability is key to ensuring that the generator and discriminator can learn from diverse data samples without being overly sensitive to specific patterns that may not represent the broader dataset. This is particularly important in scenarios where the data is inherently noisy or where different styles or features exist within the input data. A robust GAN can generalize better, producing outputs that are not only realistic but also diverse, a fundamental goal of GAN training.