  Meta-learning stands for 'learning to learn' such that generalization to new
tasks is achieved. Among these methods, Gradient-based meta-learning algorithms
are a specific sub-class that excel at quick adaptation to new tasks with
limited data. This demonstrates their ability to acquire transferable
knowledge, a capability that is central to human learning. However, the
existing meta-learning approaches only depend on the current task information
during the adaptation, and do not share the meta-knowledge of how a similar
task has been adapted before. To address this gap, we propose a 'Path-aware'
model-agnostic meta-learning approach. Specifically, our approach not only
learns a good initialization for adaptation, it also learns an optimal way to
adapt these parameters to a set of task-specific parameters, with learnable
update directions, learning rates and, most importantly, the way updates evolve
over different time-steps. Compared to the existing meta-learning methods, our
approach offers: (a) The ability to learn gradient-preconditioning at different
time-steps of the inner-loop, thereby modeling the dynamic learning behavior
shared across tasks, and (b) The capability of aggregating the learning context
through the provision of direct gradient-skip connections from the old
time-steps, thus avoiding overfitting and improving generalization. In essence,
our approach not only learns a transferable initialization, but also models the
optimal update directions, learning rates, and task-specific learning trends.
Specifically, in terms of learning trends, our approach determines the way
update directions shape up as the task-specific learning progresses and how the
previous update history helps in the current update. Our approach is simple to
implement and demonstrates faster convergence. We report significant
performance improvements on a number of FSL datasets.
