  Active learning promises to alleviate the massive data needs of supervised
machine learning: it has successfully improved sample efficiency by an order of
magnitude on traditional tasks like topic classification and object
recognition. However, we uncover a striking contrast to this promise: across 5
models and 4 datasets on the task of visual question answering, a wide variety
of active learning approaches fail to outperform random selection. To
understand this discrepancy, we profile 8 active learning methods on a
per-example basis, and identify the problem as collective outliers -- groups of
examples that active learning methods prefer to acquire but models fail to
learn (e.g., questions that ask about text in images or require external
knowledge). Through systematic ablation experiments and qualitative
visualizations, we verify that collective outliers are a general phenomenon
responsible for degrading pool-based active learning. Notably, we show that
active learning sample efficiency increases significantly as the number of
collective outliers in the active learning pool decreases. We conclude with a
discussion and prescriptive recommendations for mitigating the effects of these
outliers in future work.
