Towards Understanding the Behaviors of Optimal Deep Active Learning Algorithms

• URL: http://arxiv.org/abs/2101.00977v2
• Date: Sat, 20 Feb 2021 20:15:18 GMT
• Title: Towards Understanding the Behaviors of Optimal Deep Active Learning Algorithms
• Authors: Yilun Zhou, Adithya Renduchintala, Xian Li, Sida Wang, Yashar Mehdad, Asish Ghoshal
• Abstract summary: Active learning (AL) algorithms may achieve better performance with fewer data because the model guides the data selection process. There is little study on what the optimal AL looks like, which would help researchers understand where their models fall short. We present a simulated annealing algorithm to search for this optimal oracle and analyze it for several tasks.
• Score: 19.65665942630067
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Active learning (AL) algorithms may achieve better performance with fewer data because the model guides the data selection process. While many algorithms have been proposed, there is little study on what the optimal AL algorithm looks like, which would help researchers understand where their models fall short and iterate on the design. In this paper, we present a simulated annealing algorithm to search for this optimal oracle and analyze it for several tasks. We present qualitative and quantitative insights into the behaviors of this oracle, comparing and contrasting them with those of various heuristics. Moreover, we are able to consistently improve the heuristics using one particular insight. We hope that our findings can better inform future active learning research. The code is available at https://github.com/YilunZhou/optimal-active-learning.

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