Few-shot classification consists of a training phase where a model is learned on a relatively large dataset and an adaptation phase where the learned model is adapted to previously-unseen tasks with limited labeled samples. In this paper, we empirically prove that the training algorithm and the adaptation algorithm can be completely disentangled, which allows algorithm analysis and design to be done individually for each phase. Our meta-analysis for each phase reveals several interesting insights that may help better understand key aspects of few-shot classification and connections with other fields such as visual representation learning and transfer learning. We hope the insights and research challenges revealed in this paper can inspire future work in related directions. Code and pre-trained models (in PyTorch) are available at https://github.com/Frankluox/CloserLookAgainFewShot.

翻译：小样本分类包括两个阶段：在相对较大的数据集上学习模型的训练阶段，以及将所学模型适应于仅有少量标注样本的新任务的适应阶段。本文通过实验证明，训练算法和适应算法可以完全解耦，从而允许对每个阶段分别进行算法分析与设计。针对每个阶段进行的元分析揭示了若干有趣见解，有助于更深入理解小样本分类的关键方面及其与视觉表征学习、迁移学习等其他领域的联系。我们希望本文所揭示的见解与研究挑战能够启发性地推动相关方向的未来工作。代码与预训练模型（基于PyTorch）已开源至 https://github.com/Frankluox/CloserLookAgainFewShot。