[ICLR'22] [KDD'22] [IJCAI'24] Implementation of "Graph Condensation for Graph Neural Networks"
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Updated
Jun 28, 2024 - Python
[ICLR'22] [KDD'22] [IJCAI'24] Implementation of "Graph Condensation for Graph Neural Networks"
Official PyTorch implementation of "Dataset Condensation via Efficient Synthetic-Data Parameterization" (ICML'22)
(NeurIPS 2023 spotlight) Large-scale Dataset Distillation/Condensation, 50 IPC (Images Per Class) achieves the highest 60.8% on original ImageNet-1K val set.
[IJCAI 2024] Papers about graph reduction including graph coarsening, graph condensation, graph sparsification, graph summarization, etc.
ICLR 2024, Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching
Code for Backdoor Attacks Against Dataset Distillation
Official PyTorch Implementation for the "Distilling Datasets Into Less Than One Image" paper.
Continual Learning code for SRe2L paper (NeurIPS 2023 spotlight)
[ICLR 2024] "Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality" by Xuxi Chen*, Yu Yang*, Zhangyang Wang, Baharan Mirzasoleiman
An Efficient Dataset Condensation Plugin and Its Application to Continual Learning. NeurIPS, 2023
A collection of dataset distillation papers.
Dataset Distillation on 3D Point Clouds using Gradient Matching
Awesome Graph Condensation Papers
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