no code implementations • 18 Jan 2024 • Yutong Xia, Runpeng Yu, Yuxuan Liang, Xavier Bresson, Xinchao Wang, Roger Zimmermann
Graph Neural Networks (GNNs) have become the preferred tool to process graph data, with their efficacy being boosted through graph data augmentation techniques.
no code implementations • NeurIPS 2023 • Runpeng Yu, Xinchao Wang
In this paper, we make a bold attempt toward an ambitious task: given a pre-trained classifier, we aim to reconstruct an image generator, without relying on any data samples.
no code implementations • 29 Aug 2023 • Hong Zhu, Runpeng Yu, Xing Tang, Yifei Wang, Yuan Fang, Yisen Wang
Data in the real-world classification problems are always imbalanced or long-tailed, wherein the majority classes have the most of the samples that dominate the model training.
1 code implementation • CVPR 2023 • Runpeng Yu, Songhua Liu, Xingyi Yang, Xinchao Wang
Machine learning society has witnessed the emergence of a myriad of Out-of-Distribution (OoD) algorithms, which address the distribution shift between the training and the testing distribution by searching for a unified predictor or invariant feature representation.
no code implementations • CVPR 2023 • Songhua Liu, Jingwen Ye, Runpeng Yu, Xinchao Wang
In this paper, we explore the problem of slimmable dataset condensation, to extract a smaller synthetic dataset given only previous condensation results.
no code implementations • 12 Jun 2022 • Runpeng Yu, Hong Zhu, Kaican Li, Lanqing Hong, Rui Zhang, Nanyang Ye, Shao-Lun Huang, Xiuqiang He
Due to the poor generalization performance of traditional empirical risk minimization (ERM) in the case of distributional shift, Out-of-Distribution (OoD) generalization algorithms receive increasing attention.