Information Condensing Active Learning

• URL: http://arxiv.org/abs/2002.07916v2
• Date: Thu, 20 Feb 2020 02:52:05 GMT
• Title: Information Condensing Active Learning
• Authors: Siddhartha Jain, Ge Liu, David Gifford
• Abstract summary: We introduce Information Condensing Active Learning (ICAL), a batch mode model Active Learning (AL) method targeted at Deep Bayesian Active Learning. ICAL uses the Hilbert Schmidt Independence Criterion (HSIC) to measure the strength of the dependency between a candidate batch of points and the unlabeled set. We show significant improvements in terms of model accuracy and negative log likelihood (NLL) on several image datasets compared to state of the art batch mode AL methods for deep learning.
• Score: 4.769747792846005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Information Condensing Active Learning (ICAL), a batch mode model agnostic Active Learning (AL) method targeted at Deep Bayesian Active Learning that focuses on acquiring labels for points which have as much information as possible about the still unacquired points. ICAL uses the Hilbert Schmidt Independence Criterion (HSIC) to measure the strength of the dependency between a candidate batch of points and the unlabeled set. We develop key optimizations that allow us to scale our method to large unlabeled sets. We show significant improvements in terms of model accuracy and negative log likelihood (NLL) on several image datasets compared to state of the art batch mode AL methods for deep learning.

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