AlignPrune uses a Dynamic Alignment Score from loss trajectories to identify noisy samples more accurately than per-sample loss, improving pruning accuracy by up to 6.3% on noisy benchmarks.
Learning from Noisy Labels with Deep Neural Networks: A Survey.TNNLS, pages 1–19, 2022
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Beyond Loss Values: Robust Dynamic Pruning via Loss Trajectory Alignment
AlignPrune uses a Dynamic Alignment Score from loss trajectories to identify noisy samples more accurately than per-sample loss, improving pruning accuracy by up to 6.3% on noisy benchmarks.