TILT adds a target-data penalty on an auxiliary predictor component to induce effective importance weighting for unsupervised domain adaptation under covariate shift.
Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=
2 Pith papers cite this work. Polarity classification is still indexing.
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Semi-LAR is a semi-supervised contrastive learning framework with linear attention for nighttime flare removal that refines pseudo-labels via quality assessment and uses flare-aware patch-level contrastive losses.
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TILT: Target-induced loss tilting under covariate shift
TILT adds a target-data penalty on an auxiliary predictor component to induce effective importance weighting for unsupervised domain adaptation under covariate shift.
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Semi-LAR: Semi-supervised Contrastive Learning with Linear Attention for Removal of Nighttime Flares
Semi-LAR is a semi-supervised contrastive learning framework with linear attention for nighttime flare removal that refines pseudo-labels via quality assessment and uses flare-aware patch-level contrastive losses.