TRACK 2: Deep Learning and Optimization
Deep Learning and Optimization encompass a wide array of techniques
employed in pattern recognition and machine learning (PRML). Deep
learning, a subset of machine learning, employs artificial neural
networks with multiple layers (deep architectures) to understand and
interpret data patterns. Neural generative models, including
autoencoders and Generative Adversarial Networks (GANs), enable the
creation of new data instances resembling the training set.
Autoencoders compress data while GANs generate new instances through
a duelling network process. These tools further aid in scene
analysis and understanding, enabling machines to make sense of
complex visual environments. The field also includes multi-view
learning, transfer learning, low-shot, semi-, and unsupervised
learning methods. Multi-view learning aims to improve the model’s
performance by understanding things better from various
perspectives. Transfer learning enhances learning efficiency and
performance when a pre-trained model is used on a new problem.
Low-shot learning deals with scarce data scenarios, while
unsupervised learning identifies hidden patterns in unlabelled data.
Finally, motion and tracking engage these tools to predict and track
dynamic object movement, vital for applications such as autonomous
driving or video surveillance. All these methodologies are critical
for pushing the frontiers of PRML and its applications.
Track Chairs:
Prof. Quanxue Gao, Xidian University, China
Prof. Feiping Nie, Northwestern Polytechnical
University, China
Track Program Chairs:
Assoc. Prof. Qianqian Wang, Xidian University, China
Assoc. Prof. Ming Yang, the University of Evansville,
USA
Assoc. Prof. Deyan Xie, Qingdao Agricultural University, China
Track Technical Committee:
Dr. Xia Wei, Xidian University, China
Asst. Prof. Danyang Wu, Xi'an Jiaotong University,
China
Asst. Prof. Zheng Wang, Northwestern Polytechnical
University, China
Dr. Wenxuan Tu, National University of Defense
Technology, China
Dr. Canyu Zhang, Northwestern Polytechnical University,
China
Assoc. Prof. Han Zhang, Northwestern Polytechnical
University, China
Topics of interest include, but are not limited
to:
◆ Neural Generative Models, Autoencoders, GANs
◆ Optimization and Learning Methods
◆
Representation Learning and Deep Learning
◆
Scene Analysis and Understanding
◆ Transfer
Learning, Low-Shot, Semi- and Unsupervised Learning
◆ Motion and Tracking
Submission Guidelines
Please submit your manuscript via
Electronic
Submission System (account is needed).
(Please choose the track number when you make the submission.)
Important Dates
◆ Submission of Full Papers:
Jan. 25, 2025
◆ Notification of Review Result
of Papers from Track: Feb. 15, 2025
◆
Registration Deadline: Mar. 05, 2025