JCST CFP: Special Section on Self-Learning with Deep Neural Networks

Indexed by: SCI Expanded, EI, etc.

http://jcst.ict.ac.cn

https://www.springer.com/journal/11390

CALL FOR PAPERS

Special Section on

Self-Learning with Deep Neural Networks

Aims and Scope

Self-learning is an important skill for human beings as they journey through education and beyond to advisors, building independence and ability to progress without reliance on a teacher. Recently, as a crucial branch of Artificial Intelligence, self-learning with deep neural networks sheds its light on diverse research directions, e.g., self-supervised learning, self-distillation learning, self-attention learning, adversarial learning. Also, excellent results have been achieved in many application tasks in Computer Vision and Natural Language Processing by leveraging these self-learning approaches. Therefore, for better understanding and developing self-learning methods, it is deserved to conduct in-depth research on self-learning with deep neural networks from both theoretical and applied perspectives.

This special section of JCST journal papers will focus on theories, technologies and solutions related, but not limited to:

² Theories of self-learning, including theories of contrastive learning, theories of self-supervised learning, or theories of adversarial learning.

² Methods of self-learning, including contrastive unsupervised representation learning, self-supervised learning, self-distillation, self-attention mechanisms, etc.

² Self-learning from different and specific paradigms, e.g., generative self-learning, contrastive self-learning, adversarial learning, Transformers, learning from synthesized data.

² Self-learning approaches for specific tasks, including object detection, fine-grained visual recognition, long-tailed recognition, language understanding, etc.

Besides original research papers, we also strongly encourage high-quality survey papers, systems papers, and applications papers.

Schedule

Submission due: November 20, 2021

First Revision/Reject Notification: January 10, 2022

Final decision: March 15, 2022

Camera-Ready: March 25, 2022

Expected Publication: June 2022

Submission Procedure

All submissions must be done electronically through JCST's e-submission system at: https://mc03.manuscriptcentral.com/jcst with a manuscript type: "Special Section on Self-Learning with Deep Neural Networks".

Leading Editor

Min-Ling Zhang (Southeast University, Nanjing)

Guest Editors

Xiu-Shen Wei (Nanjing University of Science and Technology, Nanjing)

Gao Huang (Tsinghua University, Beijing)