Similarity Search for Efficient Active Learning and Search of Rare Concepts
This enables web-scale active learning for industrial applications with billions of unlabeled examples, though it is incremental as it builds on existing methods by optimizing candidate selection.
The paper tackled the computational inefficiency of active learning and search methods in large-scale industrial settings by restricting candidate selection to nearest neighbors of labeled data, achieving similar mean average precision and recall as global approaches while reducing computational cost by up to three orders of magnitude on datasets including a 10-billion-image dataset.
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 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, thus enabling web-scale active learning.