  To learn models or features that generalize across tasks and domains is one
of the grand goals of machine learning. In this paper, we propose to use
cross-domain, cross-task data as validation objective for hyper-parameter
optimization (HPO) to improve on this goal. Given a rich enough search space,
optimization of hyper-parameters learn features that maximize validation
performance and, due to the objective, generalize across tasks and domains. We
demonstrate the effectiveness of this strategy on few-shot image classification
within and across domains. The learned features outperform all previous
few-shot and meta-learning approaches.
