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.

翻译：小样本分类包含两个阶段：训练阶段（在相对较大的数据集上学习模型）和适应阶段（将已学模型应用于先前未见且仅有少量标注样本的任务）。本文通过实验证明，训练算法与适应算法可以完全解耦，这使得算法分析与设计能够分别针对每个阶段独立进行。针对各阶段的元分析揭示了几项有趣见解，有助于深入理解小样本分类的关键特性以及其与视觉表征学习、迁移学习等领域的关联。我们期望本文所揭示的见解与研究挑战能够启发相关方向的未来工作。