ImitAL: Learned Active Learning Strategy on Synthetic Data

TL;DR

ImitAL introduces a novel, learned active learning query strategy that optimally combines informativeness and representativeness heuristics, trained on synthetic data and evaluated across diverse real datasets.

Contribution

The paper presents ImitAL, a domain-independent active learning strategy that encodes query selection as a learning-to-rank problem and learns an optimal heuristic combination.

Findings

ImitAL outperforms 7 existing strategies on 13 datasets.

Training on synthetic data generalizes well to real datasets.

The learned strategy effectively balances informativeness and representativeness.

Abstract

Active Learning (AL) is a well-known standard method for efficiently obtaining annotated data by first labeling the samples that contain the most information based on a query strategy. In the past, a large variety of such query strategies has been proposed, with each generation of new strategies increasing the runtime and adding more complexity. However, to the best of our our knowledge, none of these strategies excels consistently over a large number of datasets from different application domains. Basically, most of the the existing AL strategies are a combination of the two simple heuristics informativeness and representativeness, and the big differences lie in the combination of the often conflicting heuristics. Within this paper, we propose ImitAL, a domain-independent novel query strategy, which encodes AL as a learning-to-rank problem and learns an optimal combination between both heuristics. We train ImitAL on large-scale simulated AL runs on purely synthetic datasets. To show that ImitAL was successfully trained, we perform an extensive evaluation comparing our strategy on 13 different datasets, from a wide range of domains, with 7 other query strategies.