A Closer Look at Few-shot Classification

Code

• github.com/wyharveychen/CloserLookFewShot
  OfficialIn paperpytorch★ 0
• github.com/sicara/easy-few-shot-learning
  pytorch★ 1,301
• github.com/cyvius96/few-shot-meta-baseline
  pytorch★ 653
• github.com/yinboc/few-shot-meta-baseline
  pytorch★ 653
• github.com/hu-my/taskattributedistance
  pytorch★ 143
• github.com/anujinho/trident
  pytorch★ 40
• github.com/vinuni-vishc/Few-Shot-Cosine-Transformer
  pytorch★ 31
• github.com/vinuni-vishc/few-shot-transformer
  pytorch★ 30
• github.com/mikehuisman/revisiting-learned-optimizers
  pytorch★ 5
• github.com/tjujianyu/rrl
  pytorch★ 5

Abstract

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 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.

Tasks

Benchmark Results

Reproductions