1 code implementation • 5 Jun 2024 • Yulong Zhang, Yuan YAO, Shuhao Chen, Pengrong Jin, Yu Zhang, Jian Jin, Jiangang Lu
By analyzing the learning objective of ERM, we find that the guidance information for labeled samples in a specific category is the corresponding label encoding.
1 code implementation • 6 Jan 2024 • Shuhao Chen, Yulong Zhang, Weisen Jiang, Jiangang Lu, Yu Zhang
Recent advances achieved by deep learning models rely on the independent and identically distributed assumption, hindering their applications in real-world scenarios with domain shifts.
no code implementations • 23 Sep 2023 • Yulong Zhang, Shuhao Chen, Weisen Jiang, Yu Zhang, Jiangang Lu, James T. Kwok
However, the performance of existing UDA methods is constrained by the large domain shift and limited target domain data.
no code implementations • 17 Mar 2023 • Yulong Zhang, Shuhao Chen, Yu Zhang, Jiangang Lu
The generated samples can well simulate the data distribution of the target domain and help existing UDA methods transfer from the source domain to the target domain more easily, thus improving the transfer performance.
no code implementations • 12 Dec 2020 • Jiarong Xu, Yizhou Sun, Xin Jiang, Yanhao Wang, Yang Yang, Chunping Wang, Jiangang Lu
To bridge the gap between theoretical graph attacks and real-world scenarios, in this work, we propose a novel and more realistic setting: strict black-box graph attack, in which the attacker has no knowledge about the victim model at all and is not allowed to send any queries.
no code implementations • 4 Dec 2020 • Jiarong Xu, Yang Yang, Junru Chen, Chunping Wang, Xin Jiang, Jiangang Lu, Yizhou Sun
Additionally, we explore a provable connection between the robustness of the unsupervised graph encoder and that of models on downstream tasks.