ICIP 2025 Satellite Workshop

Title: Learning Beyond Deep Learning (LBDL)

Description:

There has been a rapid development of artificial intelligence and machine learning technologies in the last decade. Although deep learning networks have significantly impacted various application domains such as computer vision, natural language processing, autonomous driving, robotics navigation, etc., they have several limitations. They are mathematically intractable, vulnerable to adversarial attacks, and demand a lot of training data. Furthermore, their training is computationally intensive, and their large model sizes make deploying mobile and edge devices a significant challenge. Developing new machine learning paradigms beyond deep learning is highly desirable. We intend to use this workshop to attract researchers of common interests and generate momentum for future breakthroughs. One or more characteristics will feature the new learning paradigm: interpretability, smaller model sizes, lower computational complexities, and high performance.

This workshop and another workshop titled “Transparent Image Processing (TIP)” of ICIP 2025 are sister workshops. LBDL focuses on learning-based models that deviate from deep learning in parts or whole. In contrast, TIP covers learning- and non-learning-based image processing algorithms emphasizing algorithmic transparency.

Submission Guidelines:

All invited and regular papers will follow the 6-page requirement of the main ICIP conference, and a technical program committee of the Workshop will review them.

Organizers:

• C.-C. Jay Kuo, University of Southern California, USA, email: jckuo@usc.edu
• Ling Guan, Toronto Metropolitan Univ/Ryerson Univ, Canada, email: lguan@ee.ryerson.ca

Technical Program Committee Members:

• Lei Gao, Toronto Metropolitan University, Canada
• Dongwoo Kang, Hongik University, Korea
• Jewon Kang, Ewha Womans University, Korea
• Ming-Sui Lee, National Taiwan University, Taiwan
• Jianquan Liu, NEC Corporation, Japan
• Xiaofeng Liu, Yale University, USA
• Bojan Mihaljevic, Universidad Politécnica de Madrid, Spain
• Paisarn Muneesawang, Mahidol University, Thailand
• Witold Pedrycz, University of Alberta, Canada
• Simon Pun, Chinese University of Hong Kong (Shenzhen), China
• Yuzhuo Ren, Nvidia, USA
• Xinchao Wang, National University of Singapore, Singapore
• Harry Yang, Hong Kong University of Science and Technology, Hong Kong
• Niclas Zeller, Hochschule Karlsruhe University of Applied Sciences, Germany