Fréchet Inception Distance (FID)

What is Fréchet Inception Distance (FID)

The Fréchet Inception Distance (FID) is a metric used to evaluate the quality and diversity of images generated by generative models, such as Generative Adversarial Networks (GANs). It measures the similarity between the distribution of generated images and the distribution of real images based on computer vision features extracted from the Inception v3 model. The FID score is particularly useful in scenarios where the goal is to produce high-quality, realistic images.

How Fréchet Inception Distance (FID) Works

The FID score is calculated by first loading a pre-trained Inception v3 model. The output layer of the model is removed and the output is taken as the activations from the last pooling layer, a global spatial pooling layer. This output layer has 2,048 activations, which are referred to as the coding vector or feature vector for the image. A 2,048 feature vector is then predicted for a collection of real images from the problem domain to provide a reference for how real images are represented. Feature vectors can then be calculated for synthetic images.

Benefits and Drawbacks of Using Fréchet Inception Distance (FID)

Benefits

1. Improved Evaluation: FID provides a more comprehensive evaluation of generated images by considering both quality and diversity.

2. Computer Vision Features: The use of computer vision features from the Inception v3 model helps in capturing the nuances of real images, making the evaluation more accurate.

3. Low Scores Indicate Better Quality: A lower FID score indicates that the generated images are more similar to real images, suggesting better quality.

Drawbacks

1. Computational Complexity: Calculating the FID score can be computationally expensive, especially for large datasets.

2. Dependence on Model: The FID score is heavily dependent on the pre-trained Inception v3 model, which may not always be the best choice for a specific problem domain.

Use Case Applications for Fréchet Inception Distance (FID)

1. Generative Adversarial Networks (GANs): FID is commonly used to evaluate the performance of GANs in generating realistic images.

2. Image Generation: FID can be used to evaluate the quality of images generated by other generative models, such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs).

3. Image-to-Image Translation: FID can be used to evaluate the quality of images generated by image-to-image translation models.

Best Practices of Using Fréchet Inception Distance (FID)

1. Use a Pre-Trained Model: Use a pre-trained Inception v3 model to ensure that the features extracted are robust and accurate.

2. Balance Sample Size: Ensure that the sample size of real and generated images is balanced to avoid bias in the evaluation.

3. Use Consistent Evaluation: Use consistent evaluation methods and parameters to ensure fair comparisons between different models and datasets.

Recap

The Fréchet Inception Distance (FID) is a widely used metric for evaluating the quality and diversity of images generated by generative models. It measures the similarity between the distribution of generated images and the distribution of real images based on computer vision features extracted from the Inception v3 model. FID provides a comprehensive evaluation of generated images and is particularly useful in scenarios where high-quality, realistic images are required. However, it has some drawbacks, such as computational complexity and dependence on the pre-trained model. By following best practices and considering the benefits and drawbacks, FID can be effectively used in various applications.