IALE: Imitating Active Learner Ensembles

Abstract

Active learning (AL) prioritizes the labeling of the most informative data samples. As the performance of well-known AL heuristics highly depends on the underlying model and data, recent heuristic-independent approaches that are based on reinforcement learning directly learn a policy that makes use of the labeling history to select the next sample. However, those methods typically need a huge number of samples to sufficiently explore the relevant state space. Imitation learning approaches aim to help out but again rely on a given heuristic. This paper proposes an improved imitation learning scheme that learns a policy for batch-mode pool-based AL. This is similar to previously presented multi-armed bandit approaches but in contrast to them we train a policy that imitates the selection of the best expert heuristic at each stage of the AL cycle directly. We use DAGGER to train the policy on a dataset and later apply it to similar datasets. With multiple AL heuristics as experts, the policy is able to reflect the choices of the best AL heuristics given the current state of the active learning process. We evaluate our method on well-known image datasets and show that we outperform state of the art imitation learners and heuristics.