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University of British Columbia, Vector Institute
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DeluluNet can adapt to new satellite sensors without extensive retraining, maintaining predictive accuracy in a rapidly evolving landscape.
A new benchmark reveals how well machine learning models can map hedgerows across diverse climates and distances, highlighting significant generalization challenges.
Current open-set TTA methods fail to effectively balance in-distribution accuracy with out-of-distribution detection, revealing a critical gap in their robustness.
Ditch unstable final-layer self-distillation: Bootleg predicts latent representations from multiple hidden layers, boosting ImageNet classification by 10% over I-JEPA.
