What metrics are commonly used for evaluating the performance of generative models?

The Inception Score and Fréchet Inception Distance are widely recognized metrics specifically designed to evaluate the performance of generative models, particularly in the domain of image generation.

The Inception Score measures the quality and diversity of generated images by analyzing how well the model-generated images can be classified by a pre-trained Inception classifier. A higher Inception Score indicates that generated images are both high-quality and varied, meaning they resemble real images and cover a broad distribution of image categories.

Fréchet Inception Distance complements this by measuring the distance between the feature distributions of generated images and real images, also using a pre-trained Inception model. This distance provides a more statistical approach, quantifying how similar the generated image distribution is to that of the real images, with lower values indicating closer similarity.

These metrics are particularly suitable for generative tasks where qualitative assessment of the generated content is essential, making them more relevant than traditional metrics like accuracy, precision, or measures used in classification tasks. Such metrics allow researchers and practitioners to better understand how well their generative models are performing in producing realistic outputs.