  We present a conceptually simple, flexible, and general framework for
few-shot learning, where a classifier must learn to recognise new classes given
only few examples from each. Our method, called the Relation Network (RN), is
trained end-to-end from scratch. During meta-learning, it learns to learn a
deep distance metric to compare a small number of images within episodes, each
of which is designed to simulate the few-shot setting. Once trained, a RN is
able to classify images of new classes by computing relation scores between
query images and the few examples of each new class without further updating
the network. Besides providing improved performance on few-shot learning, our
framework is easily extended to zero-shot learning. Extensive experiments on
five benchmarks demonstrate that our simple approach provides a unified and
effective approach for both of these two tasks.
