  Meta-learning of shared initialization parameters has shown to be highly
effective in solving few-shot learning tasks. However, extending the framework
to many-shot scenarios, which may further enhance its practicality, has been
relatively overlooked due to the technical difficulties of meta-learning over
long chains of inner-gradient steps. In this paper, we first show that allowing
the meta-learners to take a larger number of inner gradient steps better
captures the structure of heterogeneous and large-scale task distributions,
thus results in obtaining better initialization points. Further, in order to
increase the frequency of meta-updates even with the excessively long
inner-optimization trajectories, we propose to estimate the required shift of
the task-specific parameters with respect to the change of the initialization
parameters. By doing so, we can arbitrarily increase the frequency of
meta-updates and thus greatly improve the meta-level convergence as well as the
quality of the learned initializations. We validate our method on a
heterogeneous set of large-scale tasks and show that the algorithm largely
outperforms the previous first-order meta-learning methods in terms of both
generalization performance and convergence, as well as multi-task learning and
fine-tuning baselines.
