What is FID (Fréchet Inception Distance)?

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FID (Fréchet Inception Distance)

The Fréchet Inception Distance (FID) is a metric used to evaluate the quality of images generated by models like GANs (Generative Adversarial Networks) or diffusion models.

It measures how close the distribution of generated images is to the distribution of real images.

How It Works

  1. Feature Extraction

    • Both real and generated images are passed through a pre-trained Inception-v3 network (a CNN trained on ImageNet).

    • Instead of comparing raw pixels, FID compares high-level features (which capture shapes, textures, objects).

  2. Statistical Representation

    • For real and generated images, we model their feature activations as multivariate Gaussian distributions.

    • Each distribution is described by:

      • Mean (μ) → average feature vector.

      • Covariance (Σ) → how features vary together.

  3. Distance Calculation

    • FID measures the Fréchet distance (a type of Wasserstein-2 distance) between the two Gaussians:

      FID=μrμg2+Tr(Σr+Σg2(ΣrΣg)1/2)FID = ||\mu_r - \mu_g||^2 + Tr\left(\Sigma_r + \Sigma_g - 2(\Sigma_r \Sigma_g)^{1/2}\right)
    • Where:

      • μr, Σr = mean and covariance of real images

      • μg, Σg = mean and covariance of generated images

Interpretation

  • Lower FID = Better quality

    • Means generated images are more similar to real images.

  • Higher FID = Worse quality

    • Suggests generated images are blurry, unrealistic, or have artifacts.

👉 Example:

  • FID ≈ 0 → generated images are nearly indistinguishable from real ones.

  • FID > 50 → poor generation quality.

Why FID is Important?

  • Improves over earlier metrics like Inception Score (IS), which only looked at diversity but not similarity to real images.

  • Takes into account both:

    • Quality (realism of each image)

    • Diversity (variety in generated images)

In summary:
The FID (Fréchet Inception Distance) is a widely used metric to evaluate generative models by comparing feature distributions of real vs. generated images using Inception-v3 embeddings. Lower FID = higher quality & diversity of generated images.

Read more :

What is perplexity in LLM evaluation?

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