5 papers
UniDFKD: A Unified Semantic Prior Framework for Architecture-Agnostic Data-Free Knowledge Distillation
Xuewan He, Tong Chu, Zihan Cheng +5
Data-Free Knowledge Distillation (DFKD) transfers knowledge from a pretrained teacher model to a compact student model by synthesizing semantically informative data, eliminating th…
DIVER:Diving Deeper into Distilled Data via Expressive Semantic Recovery
Qianxin Xia, Zhiyong Shu, Wenbo Jiang +3
Dataset distillation aims to synthesize a compact proxy dataset that is unreadable or non-raw from the original dataset for privacy protection and highly efficient learning. Howeve…
Efficient Dataset Distillation for Pre-Trained Self-Supervised Models via Statistical Flow Matching
Qianxin Xia, Jiawei Du, Xin Zhang +3
Dataset distillation seeks to synthesize a highly compact dataset that achieves performance comparable to the original dataset on downstream tasks. For the classification task that…
EDITS: Enhancing Dataset Distillation with Implicit Textual Semantics
Qianxin Xia, Jiawei Du, Guoming Lu +2
Dataset distillation aims to synthesize a compact dataset from the original large-scale one, enabling highly efficient learning while preserving competitive model performance. Howe…
Open set label noise learning with robust sample selection and margin-guided module
Yuandi Zhao, Qianxi Xia, Yang Sun +3
In recent years, the remarkable success of deep neural networks (DNNs) in computer vision is largely due to large-scale, high-quality labeled datasets. Training directly on real-wo…