Invariant Gradient Alignment uses Logical Isomer Sets and a Continuous Gradient Conflict Mask to tighten OOD generalization bounds and boost empirical performance over ERM in reasoning distillation.
Sand-mask: An enhanced gradient masking strategy for the discovery of invariances in domain generalization
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
Evaluates four distribution shifts in sensor-based HAR, finds diversity shifts dominate, and shows 28 DG methods only marginally beat ERM while releasing open benchmarks.
FGMix learns instance weights via gradient compatibilities to perform mixup with extrapolation toward flatter minima, outperforming prior DG methods on DomainBed.
MEDIC uses dualistic meta-learning with joint domain-class matching to balance decision boundaries in open set domain generalization.
citing papers explorer
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Invariant Gradient Alignment for Robust Reasoning Distillation
Invariant Gradient Alignment uses Logical Isomer Sets and a Continuous Gradient Conflict Mask to tighten OOD generalization bounds and boost empirical performance over ERM in reasoning distillation.
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Assessing Distribution Shift in Human Activity Recognition for Domain Generalization
Evaluates four distribution shifts in sensor-based HAR, finds diversity shifts dominate, and shows 28 DG methods only marginally beat ERM while releasing open benchmarks.
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Learning Gradient-based Mixup with Extrapolation toward Flatter Minima for Domain Generalization
FGMix learns instance weights via gradient compatibilities to perform mixup with extrapolation toward flatter minima, outperforming prior DG methods on DomainBed.
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Exploring Dualistic Meta-Learning to Enhance Domain Generalization in Open Set Scenarios
MEDIC uses dualistic meta-learning with joint domain-class matching to balance decision boundaries in open set domain generalization.