Generative AI: Validation Techniques for GANs

Source: https://www.researchgate.net/figure/The-architecture-of-vanilla-GANs_fig1_340458845

There are several validation techniques for Generative Adversarial Networks (GANs), which are used to evaluate the quality and performance of the generated samples. Some of the most common validation techniques for GANs are:

1. Inception Score (IS): This technique uses a pre-trained Inception model to compute a score that measures the diversity and quality of the generated images. The score is computed based on the similarity of the generated images to the real images in terms of class distribution and visual quality.
2. Frechet Inception Distance (FID): This technique also uses a pre-trained Inception model, but computes the distance between the feature representations of the real and generated images in a high-dimensional feature space. A lower FID score indicates that the generated images are more similar to the real images.
3. Precision and Recall (PR): This technique evaluates the precision and recall of the generated samples with respect to the real samples. Precision measures the percentage of generated samples that are similar to the real samples, while recall measures the percentage of real samples that are similar to the generated samples.
4. Visual Inspection: This technique involves visually inspecting the generated samples and comparing them to the real samples. This is a subjective technique but can provide valuable insights into the visual quality and diversity of the generated samples.
5. User Studies: This technique involves conducting user studies to evaluate the perceived quality and diversity of the generated samples. This technique is more subjective and may vary depending on the preferences and biases of the participants.

Overall, it is recommended to use a combination of these validation techniques to evaluate the performance of GANs.