CalexNet aligns early-exit branch training and calibration to the cascade inference distribution via weighted sampling, survivor-subset calibration, and KL distillation to the backbone, matching or exceeding baselines on CIFAR-100 and CINIC-10 accuracy-FLOPs frontiers.
easy" inputs while occasionally extending the path for challenging but rare ones. This assumes that
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
CalexNet: Soft Cascade-Aligned Training and Calibration for Lightweight Early-Exit Branches
CalexNet aligns early-exit branch training and calibration to the cascade inference distribution via weighted sampling, survivor-subset calibration, and KL distillation to the backbone, matching or exceeding baselines on CIFAR-100 and CINIC-10 accuracy-FLOPs frontiers.