  Meta-learning approaches have addressed few-shot problems by finding
initialisations suited for fine-tuning to target tasks. Often there are
additional properties within training data (which we refer to as context), not
relevant to the target task, which act as a distractor to meta-learning,
particularly when the target task contains examples from a novel context not
seen during training. We address this oversight by incorporating a
context-adversarial component into the meta-learning process. This produces an
initialisation for fine-tuning to target which is both context-agnostic and
task-generalised. We evaluate our approach on three commonly used meta-learning
algorithms and two problems. We demonstrate our context-agnostic meta-learning
improves results in each case. First, we report on Omniglot few-shot character
classification, using alphabets as context. An average improvement of 4.3% is
observed across methods and tasks when classifying characters from an unseen
alphabet. Second, we evaluate on a dataset for personalised energy expenditure
predictions from video, using participant knowledge as context. We demonstrate
that context-agnostic meta-learning decreases the average mean square error by
30%.
