3 papers
cs.CV2025
Rectified Decoupled Dataset Distillation: A Closer Look for Fair and Comprehensive Evaluation
Xinhao Zhong, Shuoyang Sun, Xulin Gu +3
Dataset distillation aims to generate compact synthetic datasets that enable models trained on them to achieve performance comparable to those trained on full real datasets, while…
cs.CV2025
Temporal Saliency-Guided Distillation: A Scalable Framework for Distilling Video Datasets
Xulin Gu, Xinhao Zhong, Zhixing Wei +5
Dataset distillation (DD) has emerged as a powerful paradigm for dataset compression, enabling the synthesis of compact surrogate datasets that approximate the training utility of…
cs.CV2024
Going Beyond Feature Similarity: Effective Dataset Distillation based on Class-Aware Conditional Mutual Information
Xinhao Zhong, Bin Chen, Hao Fang +3
Dataset distillation (DD) aims to minimize the time and memory consumption needed for training deep neural networks on large datasets, by creating a smaller synthetic dataset that…