  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.
