  Deep Neural Networks (or DNNs) must constantly cope with distribution changes
in the input data when the task of interest or the data collection protocol
changes. Retraining a network from scratch to combat this issue poses a
significant cost. Meta-learning aims to deliver an adaptive model that is
sensitive to these underlying distribution changes, but requires many tasks
during the meta-training process. In this paper, we propose a tAsk-auGmented
actIve meta-LEarning (AGILE) method to efficiently adapt DNNs to new tasks by
using a small number of training examples. AGILE combines a meta-learning
algorithm with a novel task augmentation technique which we use to generate an
initial adaptive model. It then uses Bayesian dropout uncertainty estimates to
actively select the most difficult samples when updating the model to a new
task. This allows AGILE to learn with fewer tasks and a few informative
samples, achieving high performance with a limited dataset. We perform our
experiments using the brain cell classification task and compare the results to
a plain meta-learning model trained from scratch. We show that the proposed
task-augmented meta-learning framework can learn to classify new cell types
after a single gradient step with a limited number of training samples. We show
that active learning with Bayesian uncertainty can further improve the
performance when the number of training samples is extremely small. Using only
1% of the training data and a single update step, we achieved 90% accuracy on
the new cell type classification task, a 50% points improvement over a
state-of-the-art meta-learning algorithm.
