A differentiable histogram-matching preprocessing whose target distribution is learned end-to-end from normal-weather images improves classification accuracy on unseen fog, rain, sand, and snow images.
Zero-shot day-night domain adap- tation with a physics prior,
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Rethinking Image Histogram Matching for Image Classification
A differentiable histogram-matching preprocessing whose target distribution is learned end-to-end from normal-weather images improves classification accuracy on unseen fog, rain, sand, and snow images.