Distributed Active Learning for Image Recognition

Abstract

Developing intelligent learning algorithms under the constraint of limited manual labor is a fundamental research challenge and a problem of immense practical importance. Active learning algorithms alleviate this problem by automatically identifying the salient and exemplar instances from large amounts of unlabeled data; this tremendously reduces the human annotation effort as only a small subset of the samples, identi ed by the algorithm, needs to be labeled manually. Further, the unprecedented increase in the volume of digital data has necessitated the usage of multiple, independent computers for its storage and processing, in a given application. The need of the hour is therefore an active learning framework which can operate in a distributed setup, where the unlabeled data is partitioned across multiple computers. In this paper, we propose a novel algorithm to address this important challenge. Our algorithm requires minimal communication among the computers (over which the data is stored) and also enjoys nice theoretical properties. Our extensive empirical studies on a variety of challenging, real-world vision datasets, from different application domains, corroborate the potential of the proposed framework.