Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering

Siddharth Karamcheti, Ranjay Krishna, Li Fei-Fei, Christopher Manning


Abstract
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.
Anthology ID:
2021.acl-long.564
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7265–7281
Language:
URL:
https://aclanthology.org/2021.acl-long.564
DOI:
10.18653/v1/2021.acl-long.564
Award:
 Outstanding Paper
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.564.pdf