3 papers
cs.LG2026
Selecting Samples on Graphs: A Unified Dataset Pruning Framework for Lossless Training Acceleration
Dongyue Wu, Zilin Guo, Xiaoyu Li +4
The rapid growth of modern training datasets has significantly increased computational cost, motivating dataset pruning~(DP) methods which retain only a subset of informative sampl…
cs.CV2025
Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration
Dongyue Wu, Zilin Guo, Jialong Zuo +2
The ever-growing size of training datasets enhances the generalization capability of modern machine learning models but also incurs exorbitant computational costs. Existing data pr…
cs.CV2024
Structural Pruning via Spatial-aware Information Redundancy for Semantic Segmentation
Dongyue Wu, Zilin Guo, Li Yu +2
In recent years, semantic segmentation has flourished in various applications. However, the high computational cost remains a significant challenge that hinders its further adoptio…