A differentiable approximation of Expected Calibration Error is added to the training loss of fire detection models, with a curriculum schedule, and is reported to reduce calibration error on DFAN and EdgeFireSmoke, though the approximation omits labels.
Learning discriminative feature representation with pixel-level supervision for forest smoke recognition,
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Learning to Calibrate for Reliable Visual Fire Detection
A differentiable approximation of Expected Calibration Error is added to the training loss of fire detection models, with a curriculum schedule, and is reported to reduce calibration error on DFAN and EdgeFireSmoke, though the approximation omits labels.