Unverified50% confidenceFactExact time
A purely point-wise loss (such as MSE) drives the model to output the conditional mean, producing a blurring effect that flattens heavy precipitation centers and smooths rainbands, thereby systematically underestimating extremes.
1
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50%
Confidence
Long-term
Relevance
10/30/2026
First Seen
Sources
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Related Claims
UnverifiedQuantile loss is more robust to outliers than MSE and can implicitly characterize the asymmetry of prediction errors.74% similarUnverifiedTraditional conditional diffusion models can generate structurally realistic precipitation fields but fall short in point-wise accuracy; pure regression models have lower point-to-point error but produce blurry, smooth forecasts that lose spatial structure.74% similarUnverifiedQuantization brings a certain loss of precision, especially on long-tail, semantically ambiguous inputs, which is also one of the main sources of the capability gap between local models and large cloud models.72% similarUnverifiedTraining the student model with pixel-level loss functions (L1+MSE) caused the output images to appear gray and flat, resembling an average of all possible results—a classic failure mode of regression-based losses on generative tasks.68% similar
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