Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
Machine Learning
2020-02-25 v2 Machine Learning
Abstract
We design a new algorithm for batch active learning with deep neural network models. Our algorithm, Batch Active learning by Diverse Gradient Embeddings (BADGE), samples groups of points that are disparate and high-magnitude when represented in a hallucinated gradient space, a strategy designed to incorporate both predictive uncertainty and sample diversity into every selected batch. Crucially, BADGE trades off between diversity and uncertainty without requiring any hand-tuned hyperparameters. We show that while other approaches sometimes succeed for particular batch sizes or architectures, BADGE consistently performs as well or better, making it a versatile option for practical active learning problems.
Cite
@article{arxiv.1906.03671,
title = {Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds},
author = {Jordan T. Ash and Chicheng Zhang and Akshay Krishnamurthy and John Langford and Alekh Agarwal},
journal= {arXiv preprint arXiv:1906.03671},
year = {2020}
}