Active Learning Guided by Efficient Surrogate Learners
- URL: http://arxiv.org/abs/2301.02761v2
- Date: Sun, 17 Dec 2023 14:25:50 GMT
- Title: Active Learning Guided by Efficient Surrogate Learners
- Authors: Yunpyo An, Suyeong Park, Kwang In Kim
- Abstract summary: Re-training a deep learning model each time a single data point receives a new label is impractical.
We introduce a new active learning algorithm that harnesses the power of a Gaussian process surrogate in conjunction with the neural network principal learner.
Our proposed model adeptly updates the surrogate learner for every new data instance, enabling it to emulate and capitalize on the continuous learning dynamics of the neural network.
- Score: 25.52920030051264
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Re-training a deep learning model each time a single data point receives a
new label is impractical due to the inherent complexity of the training
process. Consequently, existing active learning (AL) algorithms tend to adopt a
batch-based approach where, during each AL iteration, a set of data points is
collectively chosen for annotation. However, this strategy frequently leads to
redundant sampling, ultimately eroding the efficacy of the labeling procedure.
In this paper, we introduce a new AL algorithm that harnesses the power of a
Gaussian process surrogate in conjunction with the neural network principal
learner. Our proposed model adeptly updates the surrogate learner for every new
data instance, enabling it to emulate and capitalize on the continuous learning
dynamics of the neural network without necessitating a complete retraining of
the principal model for each individual label. Experiments on four benchmark
datasets demonstrate that this approach yields significant enhancements, either
rivaling or aligning with the performance of state-of-the-art techniques.
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