Official PyTorch implementation of the paper "Dataset Distillation with Neural Characteristic Function: A Minmax Perspective" (NCFM) in CVPR 2025 (Highlight).

[IJCAI 2024] Papers about graph reduction including graph coarsening, graph condensation, graph sparsification, graph summarization, etc.

[ICLR'22] [KDD'22] [IJCAI'24] Implementation of "Graph Condensation for Graph Neural Networks"

(NeurIPS 2023 spotlight) Large-scale Dataset Distillation/Condensation, 50 IPC (Images Per Class) achieves the highest 60.8% on original ImageNet-1K val set.

Official PyTorch implementation of "Dataset Condensation via Efficient Synthetic-Data Parameterization" (ICML'22)

ICLR 2024, Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching

Awesome Graph Condensation Papers, TKDE'25 paper: Graph Condensation: A Survey.

Official PyTorch Implementation for the "Distilling Datasets Into Less Than One Image" paper.

Code for Backdoor Attacks Against Dataset Distillation

(CVPR 2025) Official implementation to DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation which outperforms SOTA top 1-acc by +1.3% and increases diversity per class by +5%

Optimization-free Dataset Distillation for Object Detection. Paper at: https://arxiv.org/abs/2506.01942

[ICLR 2024] "Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality" by Xuxi Chen*, Yu Yang*, Zhangyang Wang, Baharan Mirzasoleiman

(Pattern Recognition 2025) Towards Trustworthy Dataset Distillation

An Efficient Dataset Condensation Plugin and Its Application to Continual Learning. NeurIPS, 2023.

Dataset Distillation on 3D Point Clouds using Gradient Matching

Official PyTorch implementation of the paper "Dataset Distillation via the Wasserstein Metric" (ICCV 2025).

A collection of dataset distillation papers.

Continual Learning code for SRe2L paper (NeurIPS 2023 spotlight)