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Regularization and transfer learning are two popular techniques to enhancegeneralization on unseen data, which is a fundamental problem of machinelearning. Regularization techniques are versatile, as they are task- andarchitecture-agnostic, but they do not exploit a large amount of dataavailable. Transfer learning methods learn to transfer knowledge from onedomain to another, but may not generalize across tasks and architectures, andmay introduce new training cost for adapting to the target task. To bridge thegap between the two, we propose a transferable perturbation, MetaPerturb, whichis meta-learned to improve generalization performance on unseen data.MetaPerturb is implemented as a set-based lightweight network that is agnosticto the size and the order of the input, which is shared across the layers.Then, we propose a meta-learning framework, to jointly train the perturbationfunction over heterogeneous tasks in parallel. As MetaPerturb is a set-functiontrained over diverse distributions across layers and tasks, it can generalizeto heterogeneous tasks and architectures. We validate the efficacy andgenerality of MetaPerturb trained on a specific source domain and architecture,by applying it to the training of diverse neural architectures on heterogeneoustarget datasets against various regularizers and fine-tuning. The results showthat the networks trained with MetaPerturb significantly outperform thebaselines on most of the tasks and architectures, with a negligible increase inthe parameter size and no hyperparameters to tune.

MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures