What is the purpose of the "Inception Score" in evaluating GAN performance?

The Inception Score is specifically designed to assess both the quality and diversity of images generated by Generative Adversarial Networks (GANs). It operates by using a pre-trained Inception model, which classifies images into various categories. High-quality generated images should clearly belong to distinct categories, resulting in a high confidence score from the Inception model. Furthermore, to exhibit diversity, the generated images should cover a wide range of categories rather than cluster around a few. Thus, the Inception Score effectively captures these two important aspects, making it a valuable metric in evaluating GAN performance.

In contrast, other choices focus on unrelated factors. The speed of generated images, resource efficiency, and predictions about future performance do not directly pertain to assessing the visual quality or the variety of images produced by GANs. This makes the purpose of the Inception Score distinct and critical for understanding how well a GAN is performing in generating realistic and varied images.