5 citations · 5 across the 10 of their papers we have counts for
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Mind Your Margin and Boundary: Are Your Distilled Datasets Truly Robust?
Muquan Li, Yingyi Ma, Yihong Huang +5
Dataset distillation (DD) compresses a large training set into a small synthetic set for efficient training, but most DD methods optimize only clean accuracy and leave robustness u…
Fixed Anchors Are Not Enough: Dynamic Retrieval and Persistent Homology for Dataset Distillation
Muquan Li, Hang Gou, Yingyi Ma +3
Decoupled dataset distillation (DD) compresses large corpora into a few synthetic images by matching a frozen teacher's statistics. However, current residual-matching pipelines rel…
Beyond Random: Automatic Inner-loop Optimization in Dataset Distillation
Muquan Li, Hang Gou, Dongyang Zhang +4
The growing demand for efficient deep learning has positioned dataset distillation as a pivotal technique for compressing training dataset while preserving model performance. Howev…
Adaptive Dataset Quantization
Muquan Li, Dongyang Zhang, Qiang Dong +2
Contemporary deep learning, characterized by the training of cumbersome neural networks on massive datasets, confronts substantial computational hurdles. To alleviate heavy data st…
Towards Effective Data-Free Knowledge Distillation via Diverse Diffusion Augmentation
Muquan Li, Dongyang Zhang, Tao He +3
Data-free knowledge distillation (DFKD) has emerged as a pivotal technique in the domain of model compression, substantially reducing the dependency on the original training data.…