Deep Active Learning over the Long Tail

Yonatan Geifman; Ran El-Yaniv

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arXiv · 1711.00941

Deep Active Learning over the Long Tail

Yonatan Geifman·Ran El-Yaniv

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Abstract

This paper is concerned with pool-based active learning for deep neural networks. Motivated by coreset dataset compression ideas, we present a novel active learning algorithm that queries consecutive points from the pool using farthest-first traversals in the space of neural activation over a representation layer. We show consistent and overwhelming improvement in sample complexity over passive learning (random sampling) for three datasets: MNIST, CIFAR-10, and CIFAR-100. In addition, our algorithm outperforms the traditional uncertainty sampling technique (obtained using softmax activations), and we identify cases where uncertainty sampling is only slightly better than random sampling.

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BibTeX RIS

Yonatan Geifman, Ran El-Yaniv. 2017-11-02. Deep Active Learning over the Long Tail. https://arxiv.org/abs/1711.00941

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