Which aspect of evaluation does the Inception Score focus on?

The Inception Score is specifically designed to assess the quality and diversity of images generated by generative models, such as Generative Adversarial Networks (GANs). It measures how well the generated images fit into the categories of images that a pre-trained Inception model recognizes. High-quality generated images typically score well on the Inception Score if they are clear and belong to identifiable classes, while also accounting for the diversity of the generated outputs by confirming they belong to multiple categories rather than being limited to a few.

The other aspects mentioned, such as speed of generation, resource consumption during training, and historical performance metrics, do not pertain to the evaluation criteria that the Inception Score is intended to measure. Instead, these factors relate to other considerations in generative model training and evaluation. Focusing on both the clarity of the generated images and the variety in generated content makes the Inception Score a valuable tool for understanding how well a model performs in generating realistic data.