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The goal of few-shot learning is to learn a model that can recognize novel classes based on one or few training data. It is challenging mainly due to two aspects: (1) it lacks good feature representation of novel classes; (2) a few of labeled data could not accurately represent the true data distribution and thus it's hard to learn a good decision function for classification. In this work, we use a sophisticated network architecture to learn better feature representation and focus on the secondarXiv:2001.08366v2 fatcat:4wz7bja2vzgnhou44vhkwfkm34