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A common problem practitioners face is to select rare events in a large dataset. Unfortunately, standard techniques ranging from pre-trained models to active learning do not leverage proximity structure present in many datasets and can lead to worse-than-random results. To address this, we propose EZMODE, an algorithm for iterative selection of rare events in large, unlabeled datasets. EZMODE leverages active learning to iteratively train classifiers, but chooses the easiest positive examples to label in contrast to standard uncertainty techniques. EZMODE also leverages proximity structure (e.g., temporal sampling) to find difficult positive examples. We show that EZMODE can outperform baselines by up to 130× on a novel, real-world, 9,000 GB video dataset.
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Similarity Search for Efficient Active Learning and Search of Rare Concepts
Many active learning and search approaches are intractable for large-scale industrial settings with billions of unlabeled examples. Existing approaches search globally for the optimal examples to label, scaling linearly or even quadratically with the unlabeled data. In this paper, we improve the computational efficiency of active learning and search methods by restricting the candidate pool for labeling to the nearest neighbors of the currently labeled set instead of scanning over all of the unlabeled data. We evaluate several selection strategies in this setting on three large-scale computer vision datasets: ImageNet, OpenImages, and a de-identified and aggregated dataset of 10 billion publicly shared images provided by a large internet company. Our approach achieved similar mean average precision and recall as the traditional global approach while reducing the computational cost of selection by up to three orders of magnitude, enabling web-scale active learning.
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- Award ID(s):
- 2023239
- PAR ID:
- 10333348
- Date Published:
- Journal Name:
- Proceedings of the AAAI Conference on Artificial Intelligence
- ISSN:
- 2159-5399
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
