Title: Invariance-Guided Feature Evolution  for  Few-Shot Learning. Abstract: Few-shot learning (FSL) aims to characterize the inherent visual relationship between support and query samples which can be well generalized to unseen classes so that we can accurately infer the labels of query samples from very few support samples. We observe that, in a successfully learned FSL model, this visual relationship and the learned features of the query samples should remain largely invariant across different configurations of the support set. Driven by this observation, we propose to construct a feature evolution network with an ensemble of few-shot learners evolving along different configuration dimensions. We choose to study two major parameters that control the support set configuration: the number of labeled samples per class (called shots) and the percentage of training samples (called partition) in the support set. Based on this network, we characterize and track the evolution behavior of learned query features across different shots-partition configurations, which will be minimized by a set of invariance loss functions during the training stage. Our extensive experimental results demonstrate that the proposed invariance-guided feature evolution (IGFE) method significantly improves the performance and generalization capability of few-shot learning and outperforms the state-of-the-art methods by large margins, especially in cross-domain classification tasks for generalization capability test. For example, in the cross-domain test on the fine-grained CUB image classification task, our method has improved the classification accuracy by more than 5%.