Description
Dataset pruning is an approach to reduce a large dataset to obtain a small dataset by removing less significant sample.
Papers Using This Method
Video-XL-Pro: Reconstructive Token Compression for Extremely Long Video Understanding2025-03-24Training-Free Dataset Pruning for Instance Segmentation2025-03-02Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty2025-02-10Swift Cross-Dataset Pruning: Enhancing Fine-Tuning Efficiency in Natural Language Understanding2025-01-05Video Set Distillation: Information Diversification and Temporal Densification2024-11-28Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning2024-11-24SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency2024-11-14GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning2024-10-17Generalized Group Data Attribution2024-10-13SFPrompt: Communication-Efficient Split Federated Fine-Tuning for Large Pre-Trained Models over Resource-Limited Devices2024-07-24Automatic Pruning of Fine-tuning Datasets for Transformer-based Language Models2024-07-11Federated Learning with a Single Shared Image2024-06-18PruNeRF: Segment-Centric Dataset Pruning via 3D Spatial Consistency2024-06-02FairDeDup: Detecting and Mitigating Vision-Language Fairness Disparities in Semantic Dataset Deduplication2024-04-24A Study in Dataset Pruning for Image Super-Resolution2024-03-25Exploring Learning Complexity for Efficient Downstream Dataset Pruning2024-02-08Dataset Difficulty and the Role of Inductive Bias2024-01-03Not All Data Matters: An End-to-End Adaptive Dataset Pruning Framework for Enhancing Model Performance and Efficiency2023-12-09Spanning Training Progress: Temporal Dual-Depth Scoring (TDDS) for Enhanced Dataset Pruning2023-11-22You Only Condense Once: Two Rules for Pruning Condensed Datasets2023-10-21