  Few-shot learning with sequence-processing neural networks (NNs) has recently
attracted a new wave of attention in the context of large language models. In
the standard N-way K-shot learning setting, an NN is explicitly optimised to
learn to classify unlabelled inputs by observing a sequence of NK labelled
examples. This pressures the NN to learn a learning algorithm that achieves
optimal performance, given the limited number of training examples. Here we
study an auxiliary loss that encourages further acceleration of few-shot
learning, by applying recently proposed bootstrapped meta-learning to NN
few-shot learners: we optimise the K-shot learner to match its own performance
achievable by observing more than NK examples, using only NK examples.
Promising results are obtained on the standard Mini-ImageNet dataset. Our code
is public.
