Unverified50% confidenceFactExact time
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.
1
Sources
50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
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Related Claims
UnverifiedFID和LPIPS等视觉质量指标可以衡量图像逼真程度，但无法捕捉物理合理性75% similarUnverifiedCLIP 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.74% similarUnverifiedResolution differences indirectly affect perceived image quality; low-resolution output is usually weaker in edge sharpness, texture detail, and noise performance, and the human eye easily misjudges greater blurriness as worse model capability.74% similarUnverifiedFeature-point methods are more robust to illumination changes and motion blur but discard a large amount of image information, while direct methods can build semi-dense or even dense maps but are sensitive to illumination changes and require good initial values.72% similarVerifiedFID measures the distributional distance between generated images and real images in feature space; a lower value indicates higher generation quality.72% similar
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