Fréchet inception distance (FID) compares image distributions
Image: Morio, CC BY-SA 3.0, via Wikimedia Commons
Fréchet inception distance
Fréchet inception distance (FID) compares image distributions
The FID metric evaluates the quality of generated images by comparing their statistical distribution to that of real images. It uses mean and covariance statistics of multiple images to assess the similarity between the generated and real image sets.
Example
If a generative model produces images with a mean and covariance similar to those of a reference set of real images, the FID score will be low, indicating high quality.
Understanding FID helps in improving generative models by providing a quantitative measure of image quality.
Related concepts
IS (Inception Score) measures: diversity and quality of generated images
Inception Score quantifies diversity and quality of generated images
Effect size
Cohen's D benchmarks: 0.2 = small, 0.5 = medium, 0.8 = large effect
cosine similarity works better than Euclidean distance in high dimensions
Cosine similarity measures orientation, not magnitude, making it more robust to irrelevant dimensions in high-dimensional spaces
Short-time Fourier transform
STFT divides a signal into shorter segments for analysis
Euclidean geometry
Euclidean distance measures absolute position in space
Chebyshev distance
Chebyshev distance is named after Pafnuty Chebyshev
One email a day: 5 concepts + the 5 stories that matter →
Swipe through 100 ML concepts daily
Open TickerNews