  Few-shot classification aims to learn a classifier to recognize unseen
classes during training with limited labeled examples. While significant
progress has been made, the growing complexity of network designs,
meta-learning algorithms, and differences in implementation details make a fair
comparison difficult. In this paper, we present 1) a consistent comparative
analysis of several representative few-shot classification algorithms, with
results showing that deeper backbones significantly reduce the performance
differences among methods on datasets with limited domain differences, 2) a
modified baseline method that surprisingly achieves competitive performance
when compared with the state-of-the-art on both the \miniI and the CUB
datasets, and 3) a new experimental setting for evaluating the cross-domain
generalization ability for few-shot classification algorithms. Our results
reveal that reducing intra-class variation is an important factor when the
feature backbone is shallow, but not as critical when using deeper backbones.
In a realistic cross-domain evaluation setting, we show that a baseline method
with a standard fine-tuning practice compares favorably against other
state-of-the-art few-shot learning algorithms.
