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.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Vision Transformer with CLAHE preprocessing, two-stage fine-tuning, MixUp/CutMix, EMA, TTA, and attention rollout achieves 99.29% accuracy and 99.25% macro F1 on four-class brain tumor MRI classification from 7023 scans.
Multi-U-net model fuses multi-temporal Sentinel-1 and Sentinel-2 data to estimate pixel-wise LAI, reporting 0.06 RMSE and 0.93 R² on public data.
citing papers explorer
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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.
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an interpretable vision transformer framework for automated brain tumor classification
Vision Transformer with CLAHE preprocessing, two-stage fine-tuning, MixUp/CutMix, EMA, TTA, and attention rollout achieves 99.29% accuracy and 99.25% macro F1 on four-class brain tumor MRI classification from 7023 scans.
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Leveraging Multi-Temporal Sentinel 1 and 2 Satellite Data for Leaf Area Index Estimation With Deep Learning
Multi-U-net model fuses multi-temporal Sentinel-1 and Sentinel-2 data to estimate pixel-wise LAI, reporting 0.06 RMSE and 0.93 R² on public data.