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Our method, called the Relation Network (RN), is\ntrained end-to-end from scratch. During meta-learning, it learns to learn a\ndeep distance metric to compare a small number of images within episodes, each\nof which is designed to simulate the few-shot setting. Once trained, a RN is\nable to classify images of new classes by computing relation scores between\nquery images and the few examples of each new class without further updating\nthe network. Besides providing improved performance on few-shot learning, our\nframework is easily extended to zero-shot learning. Extensive experiments on\nfive benchmarks demonstrate that our simple approach provides a unified and\neffective approach for both of these two tasks.","url_abs":"http://arxiv.org/abs/1711.06025v2","url_pdf":"http://arxiv.org/pdf/1711.06025v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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