Deep Bayesian Active Learning with Image Data

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, we have to handle 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 acquisition functions rely on model uncertainty. In deep learning on the other hand we rarely represent such model uncertainty. Relying on Bayesian approaches to deep learning, 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 significant improvement on existing active learning approaches.