Target-aligned data selection via normalized endpoint loss drop on a validation-induced reference path achieves competitive performance with reduced computational overhead.
Grad-match: Gradient matching based data subset selection for efficient deep model training
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Introduces gradient-discrepancy acquisition criterion derived from Luo et al. (2022) generalization bound for active learning.
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Let the Target Select for Itself: Data Selection via Target-Aligned Paths
Target-aligned data selection via normalized endpoint loss drop on a validation-induced reference path achieves competitive performance with reduced computational overhead.
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Gradient-Discrepancy Acquisition for Pool-Based Active Learning
Introduces gradient-discrepancy acquisition criterion derived from Luo et al. (2022) generalization bound for active learning.