Active Learning in CNNs via Expected Improvement Maximization
- URL: http://arxiv.org/abs/2011.14015v1
- Date: Fri, 27 Nov 2020 22:06:52 GMT
- Title: Active Learning in CNNs via Expected Improvement Maximization
- Authors: Udai G. Nagpal, David A Knowles
- Abstract summary: "Dropout-based IMprOvementS" (DEIMOS) is a flexible and computationally-efficient approach to active learning.
Our results demonstrate that DEIMOS outperforms several existing baselines across multiple regression and classification tasks.
- Score: 2.0305676256390934
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep learning models such as Convolutional Neural Networks (CNNs) have
demonstrated high levels of effectiveness in a variety of domains, including
computer vision and more recently, computational biology. However, training
effective models often requires assembling and/or labeling large datasets,
which may be prohibitively time-consuming or costly. Pool-based active learning
techniques have the potential to mitigate these issues, leveraging models
trained on limited data to selectively query unlabeled data points from a pool
in an attempt to expedite the learning process. Here we present "Dropout-based
Expected IMprOvementS" (DEIMOS), a flexible and computationally-efficient
approach to active learning that queries points that are expected to maximize
the model's improvement across a representative sample of points. The proposed
framework enables us to maintain a prediction covariance matrix capturing model
uncertainty, and to dynamically update this matrix in order to generate diverse
batches of points in the batch-mode setting. Our active learning results
demonstrate that DEIMOS outperforms several existing baselines across multiple
regression and classification tasks taken from computer vision and genomics.
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