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Few-shot learning (FSL) requires a model to classify new samples afterlearning from only a few samples. While remarkable results are achieved inexisting methods, the performance of embedding and metrics determines the upperlimit of classification accuracy in FSL. The bottleneck is that deep networksand complex metrics tend to induce overfitting in FSL, making it difficult tofurther improve the performance. Towards this, we propose plug-and-playmodel-adaptive resizer (MAR) and adaptive similarity metric (ASM) without anyother losses. MAR retains high-resolution details to alleviate the overfittingproblem caused by data scarcity, and ASM decouples the relationship betweendifferent metrics and then fuses them into an advanced one. Extensiveexperiments show that the proposed method could boost existing methods on twostandard dataset and a fine-grained datasets, and achieve state-of-the-artresults on mini-ImageNet and tiered-ImageNet.

An Adaptive Plug-and-Play Network for Few-Shot Learning