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Active learning (AL) is useful for incremental model training while concurrently selecting the most informative yet minimum amount of training samples for the human annotator to label. In this paper, We introduce a pool-based efficient task-aware semi-supervised active learning (TASSAL) strategy with a modified selection probability distribution on initial sampling. In contrast to the recent approaches that separately train the core task model from the encoding models, our method allows end-to-end and simultaneous learning on both the encoding and core task model. We demonstrate that TASSAL shows a promising result and, from our experiments, it could outperform recent approaches on the CIFAR-10 dataset. In addition, evaluation of the pseudo-labeled samples against ground-truth labels shows that such an approach could potentially yield additional data to train on, which supposedly is beneficial for the downstream tasks.
Author Information
Erick Chandra (Intel)
Ramesh Manuvinakurike (Intel)
Saurav Sahay (Intel)
Sahisnu Mazumder (Intel)
Ranganath Krishnan (Intel Labs)
Jane Yung-jen Hsu (National Taiwan University)
Jane Hsu is a Professor of the Department of Computer Science and Information Engineering at National Taiwan University, where she served as the Department Chair in 2011-2014. As the Director of the NTU IoX Center, Prof. Hsu conducted global research collaboration on Internet of Things (2011-2015), Augmented Collective Beings (2016-2020), human-AI/IoT collaboration and Digital Closeness (2021-). She has been actively involved in AAAI, TAAI, IEEE, and ACM conferences, and served as the President of the Taiwanese Association for Artificial Intelligence in 2013-14. She received the 2020 NTU Pegatron Chair Professor, the 2016 MSRA Collaborative Research Award, and Intel Labs Distinguished Collaborator Award.
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