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Date
2021Type
- Conference Paper
ETH Bibliography
yes
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Abstract
Active learning is an effective technique for reducing the labeling cost by improving data efficiency. In this work, we propose a novel batch acquisition strategy for active learning in the setting where the model training is performed in a semi-supervised manner. We formulate our approach as a data summarization problem via bilevel optimization, where the queried batch consists of the points that best summarize the unlabeled data pool. We show that our method is highly effective in keyword detection tasks in the regime when only few labeled samples are available. Show more
Publication status
publishedExternal links
Book title
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)Pages / Article No.
Publisher
IEEEEvent
Subject
batch active learning; semi-supervised learning; bilevel optimization; coresetsOrganisational unit
03908 - Krause, Andreas / Krause, Andreas
Funding
167212 - Scaling Up by Scaling Down: Big ML via Small Coresets (SNF)
815943 - Reliable Data-Driven Decision Making in Cyber-Physical Systems (EC)
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ETH Bibliography
yes
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