Seminar: Training with Few Annotations: Active Self-Semi-Supervised Learning, 10th February, 1pm

When: Thursday 10th of February, 1pm AEDT

Where: This seminar will be presented online via Zoom, RSVP here.

Speaker: Ziting Wen

Title: Training with Few Annotations: Active Self-Semi-Supervised Learning

Abstract:
Part of the power of deep learning comes from massive amounts of labeled data. However, the expensive annotation cost hinders their application in professional fields, such as requiring sufficient biological knowledge to annotate the species of coral pictures. This presentation aims to provide some current progress on reducing annotations required for model training, such as active learning, semi-supervised learning, and self-supervised learning, as well as discuss how they can be combined to allow us to quickly train models using few annotations.

Bio: Ziting Wen received his Master’s degree in information and communication engineering from Shanghai Jiao Tong University, China in 2020. Since then, he has been a PhD student in the marine group, ACFR. His research interests include active learning and semi-supervised learning.

Contacts

Sydney Institute for Robotics and Intelligent Systems
info@acfr.usyd.edu.au