IJCNN2023: Call for Papers

IJCNN2023 Special Session: Call for Papers by Jan 31

IJCNN is the premier international conference in the area of neural networks theory, analysis and applications.

Special Session Abstract

This special session aims to provide a forum for researchers in the domain adaptation field to share the latest advances in domain adaptation theories, algorithms, and applications, especially how domain adaptation techniques can be applied to several areas to solve more real-world predictions and decision-making problems. 

Topics

The topics of this special session include, but are not limited to, the following:

• New domain adaptation framework and theories;
• Deep learning domain adaptation;
• Adversarial learning methods for domain adaptation;
• Homogeneous and heterogeneous domain adaptations: algorithms and applications;
• Open-set, partial-set and universal domain adaptations: algorithms and applications;
• Multiple source domain adaptation: algorithms and applications;
• Weakly supervised domain adaptation: algorithms and applications;
• Domain generalization and out-of-distribution generalization for handling the robustness of domain adaptation;
• Novel algorithms and applications of source-free domain adaptation;
• Few-shot domain adaptation: algorithms and applications;
• Various demonstration and applications of domain adaptation, such as transport, industry 4.0, business intelligence, healthcare and more.

Submit your paper here.

Organisers

• Dr. Zhen Fang, University of Technology Sydney, zhen.fang@uts.edu.au
• Dist. Prof. Jie Lu, University of Technology Sydney, jie.lu@uts.edu.au
• Prof. Jun Wang, City University of Hong Kong, jwang.cs@cityu.edu.hk
• Prof. Luis Martínez, University of Jaén, Spain, luis.martinez@ujaen.es
• Prof. Ivor W Tsang, A*STAR Centre, Singapore, ivor_tsang@ihpc.a star.edu.sg
• Dr. Hua Zuo, University of Technology Sydney, hua.zuo@uts.edu.au
• Dr. Feng Liu, University of Melbourne, feng.liu1@unimelb.edu.au
• Prof. Guangquan Zhang, University of Technology Sydney, guangquan.zhang@uts.edu