FID
A widely used metric for evaluating the quality of image generative models. It measures generation quality by computing the distance between the distributions of generated and real images in the feature space of an Inception network, with lower scores indicating higher generation quality.
Core Facts
FID measures the distributional distance between generated images and real images in feature space; a lower value indicates higher generation quality.
FID (Fréchet Inception Distance) is a quantitative metric used to measure the distance between the distribution of generated images and real photographs.
Timeline (last 90 days)
The Real Experience Score is the core metric provided by Speed Insights, computed as a score from 0 to 100 based on real user monitoring data that combines Core Web Vitals such as FCP, LCP, FID and CLS.
The most effective adaptive attack that removes the SpectralCanary signal requires aggressive image modifications and causes an increase in ΔFID of more than 25 points.
On five datasets using the SD3.5 model, SpectralCanary preserves the visual quality of released images (SSIM 0.989, LPIPS 0.022) with a near-zero FID change (average ΔFID 0.05).
Selecting target generators using editing quality metrics such as FID and CLIP Score may pick models unsuitable as control targets.
FID measures the difference between the generated and real distributions, IS evaluates the diversity and clarity of generated samples, and CLIP Score measures the degree of image-text alignment.
The FID metric is insensitive to differences in high-frequency details (such as sharp edges and fine textures), so generated images may appear visually blurry or "overly smooth" while the FID score remains good.
With only a small amount of additional training, ConvRefiner can restore the high-frequency statistical properties of generated images while keeping FID from degrading.
The paper did not disclose FID scores.
FID measures the statistical distance between the generated distribution and the real distribution, with lower values being better, but it is heavily influenced by the feature extractor and sample size, and fluctuates noticeably on small datasets.
MiniDog's evaluation covers FID, HPSv2, PickScore, and various VAE reconstruction metrics.
22 more timeline events
All Facts (20)
FID measures the distributional distance between generated images and real images in feature space; a lower value indicates higher generation quality.
80%VerifiedFID (Fréchet Inception Distance) is a quantitative metric used to measure the distance between the distribution of generated images and real photographs.
75%UnverifiedFID is calculated by comparing the differences in means and covariance matrices between real images and generated images in the feature space of the Inception-v3 network, but it is insensitive to local semantic quality.
70%UnverifiedFID (Fréchet Inception Distance) evaluates generation quality by comparing the statistical distribution differences between real and generated data in the feature space of a deep neural network, with lower values indicating closer resemblance to real data.
70%UnverifiedThe Real Experience Score is the core metric provided by Speed Insights, computed as a score from 0 to 100 based on real user monitoring data that combines Core Web Vitals such as FCP, LCP, FID and CLS.
50%UnverifiedThe most effective adaptive attack that removes the SpectralCanary signal requires aggressive image modifications and causes an increase in ΔFID of more than 25 points.
50%UnverifiedOn five datasets using the SD3.5 model, SpectralCanary preserves the visual quality of released images (SSIM 0.989, LPIPS 0.022) with a near-zero FID change (average ΔFID 0.05).
50%UnverifiedSelecting target generators using editing quality metrics such as FID and CLIP Score may pick models unsuitable as control targets.
50%UnverifiedFID measures the difference between the generated and real distributions, IS evaluates the diversity and clarity of generated samples, and CLIP Score measures the degree of image-text alignment.
50%UnverifiedWith only a small amount of additional training, ConvRefiner can restore the high-frequency statistical properties of generated images while keeping FID from degrading.
50%UnverifiedThe FID metric is insensitive to differences in high-frequency details (such as sharp edges and fine textures), so generated images may appear visually blurry or "overly smooth" while the FID score remains good.
50%UnverifiedThe paper did not disclose FID scores.
50%UnverifiedFID measures the statistical distance between the generated distribution and the real distribution, with lower values being better, but it is heavily influenced by the feature extractor and sample size, and fluctuates noticeably on small datasets.
50%UnverifiedMiniDog's evaluation covers FID, HPSv2, PickScore, and various VAE reconstruction metrics.
50%UnverifiedFID is affected by the number of samples, preprocessing and the Inception version, and cross-paper comparisons should be based on the same evaluation protocol.
50%UnverifiedOn the class-conditional ImageNet-1K benchmark, PixelMoE achieves FID 1.47 at 256×256 resolution and FID 1.53 at 512×512, without using representation alignment and perceptual losses.
50%UnverifiedCLIP score measures semantic consistency rather than visual quality and cannot capture perceptual issues such as image blurriness, artifacts, or color distortion; in practice it is typically used in combination with SSIM or FID.
50%UnverifiedPhysics-IQ measures the correctness of causal reasoning, which differs from traditional visual quality metrics such as FID that measure the perceptual realism of images.
50%UnverifiedOn the ImageNet-256 dataset, with NFE of 25 and no guidance, the time-lag-measurement-based gain selection method reduces FID from 28.0 to 12.2.
50%UnverifiedThe FID value of 12.2 is estimated by linear interpolation of FID measurements from adjacent gain values.
50%