The paper studies the problem of training a neural network to perform a given task using only a few examples. The authors propose a method called "few-shot learning" that uses a small set of labeled examples to train a model that can then be used to perform the task on new, unseen examples. The method is shown to be effective on a variety of tasks, including image classification, object detection, and natural language processing.
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