Seminar: Active Learning Meets Self-supervised Model: Effectiveness and Efficiency, 01st June, 1pm

When: Thursday 1st of June, 1pm AEST

Where: This seminar will be partially presented at the Rose Street Seminar area (J04) and partially online via Zoom. RSVP

Speaker: Ziting Wen

Title: Active Learning Meets Self-supervised Model: Effectiveness and Efficiency


The high cost of annotation plagues the application of deep learning models. Active learning has emerged as a classic approach to alleviate this issue by selecting the most informative samples to annotate. On the other hand, self-supervised training has proven to be effective in leveraging unlabeled data to generate powerful feature encoders. In this seminar, I will demonstrate how to integrate the strengths of both methodologies to design active learning strategies that operate on top of self-supervised models based on our recent work. By doing so, we aim to enhance the effectiveness and efficiency of the annotation process.


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.


Australian Centre for Robotics