Why is proper evaluation crucial for GANs?

Proper evaluation is crucial for Generative Adversarial Networks (GANs) primarily because it ensures that the generated outputs meet quality and performance benchmarks. Evaluating GANs typically involves several metrics and techniques that help assess how well the generator produces data that is indistinguishable from real data. The quality of the output is essential for applications that rely on the realism of the generated content, such as image synthesis, text generation, or any function where the end product needs to adhere to certain standards or characteristics.

Through rigorous evaluation, practitioners can identify whether the GAN is overfitting, suffering from mode collapse, or producing outputs that do not reflect the desired distribution. This process also helps in refining GAN architectures and improving training techniques. Without effective evaluation measures, it would be nearly impossible to determine if a GAN is functioning effectively, thus impacting the reliability and applicability of the model in real-world scenarios.