Towards Understanding the Optimal Behaviors of Deep Active Learning Algorithms
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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 different tasks. We present several qualitative and quantitative insights into the optimal behavior and contrast this behavior with those of various heuristics. When augmented by with one particular insight, heuristics perform consistently better. We hope that our findings can better inform future active learning research. The code for the experiments is available at https://github.com/YilunZhou/optimal-active-learning.
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