Model Evaluation and Validation

Techniques for assessing the performance of generative models

The evaluation of generative models, which include GANs, VAEs, and transformer-based models, requires the use of a variety of methods to evaluate the quality, diversity, and realism of the outputs that are created. In contrast to conventional machine learning models, which typically are evaluated using measures such as accuracy and F1-score, generative models need the use of specific evaluation techniques.

The following are some common methods for evaluating generative models:

Visual inspection is the process of personally viewing the samples that have been prepared to evaluate their quality and level of genuineness. Even though it is subjective, it offers a rapid and intuitive understanding of the performance of the model.

The Inception Score (IS) is a score that involves the use of a pre-trained Inception network to assess the variety and quality of the pictures that are created. When the IS is greater, it suggests that the pictures that are created are not just diversified but also quite comparable to genuine images.

Fréchet Inception Distance (FID) is a technique that compares the distributions of authentic pictures and images that have been produced inside the feature space of a network that has been pre-trained. The FID values that are lower imply that the produced photos are more comparable to the ones that were captured.

A metric known as Perceptual Path Length (PPL) is used to evaluate the degree of smoothness and realism of interpolations between samples that have been created. PPL values that are lower suggest that the interpolations are more realistic.

Recall and Precision for Generative Models: These metrics measure the fidelity and variety of the produced samples by comparing them to actual samples in the feature space. They do this by comparing the generated samples to the genuine samples. In contrast, recall is a measurement of variety, whereas precision is a measurement of quality.

Human Evaluation: In some instances, human evaluators are required to appraise the quality and realism of the outputs that have been created throughout the process. Even though it is expensive and time-consuming, this approach offers very helpful insights about the subjective quality of the completed work.