Title: Deep Bayesian Active Learning with Image Data,
Abstract: Even though active learning forms an important pillar of machine learning,
deep learning tools are not prevalent within it. Deep learning poses several
difficulties when used in an active learning setting. First, active learning
(AL) methods generally rely on being able to learn and update models from small
amounts of data. Recent advances in deep learning, on the other hand, are
notorious for their dependence on large amounts of data. Second, many AL
acquisition functions rely on model uncertainty, yet deep learning methods
rarely represent such model uncertainty. In this paper we combine recent
advances in Bayesian deep learning into the active learning framework in a
practical way. We develop an active learning framework for high dimensional
data, a task which has been extremely challenging so far, with very sparse
existing literature. Taking advantage of specialised models such as Bayesian
convolutional neural networks, we demonstrate our active learning techniques
with image data, obtaining a significant improvement on existing active
learning approaches. We demonstrate this on both the MNIST dataset, as well as
for skin cancer diagnosis from lesion images (ISIC2016 task).