  In few-shot learning, typically, the loss function which is applied at test
time is the one we are ultimately interested in minimising, such as the
mean-squared-error loss for a regression problem. However, given that we have
few samples at test time, we argue that the loss function that we are
interested in minimising is not necessarily the loss function most suitable for
computing gradients in a few-shot setting. We propose VIABLE, a generic
meta-learning extension that builds on existing meta-gradient-based methods by
learning a differentiable loss function, replacing the pre-defined inner-loop
loss function in performing task-specific updates. We show that learning a loss
function capable of leveraging relational information between samples reduces
underfitting, and significantly improves performance and sample efficiency on a
simple regression task. Furthermore, we show VIABLE is scalable by evaluating
on the Mini-Imagenet dataset.
