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Handling previously unseen tasks after given only a few training examplescontinues to be a tough challenge in machine learning. We propose TapNets,neural networks augmented with task-adaptive projection for improved few-shotlearning. Here, employing a meta-learning strategy with episode-based training,a network and a set of per-class reference vectors are learned across widelyvarying tasks. At the same time, for every episode, features in the embeddingspace are linearly projected into a new space as a form of quick task-specificconditioning. The training loss is obtained based on a distance metric betweenthe query and the reference vectors in the projection space. Excellentgeneralization results in this way. When tested on the Omniglot, miniImageNetand tieredImageNet datasets, we obtain state of the art classificationaccuracies under various few-shot scenarios.

TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning