Few-shot learning aims to recognize novel classes with few examples.
Pre-training based methods effectively tackle the problem by pre-training a
feature extractor and then fine-tuning it through the nearest centroid based
meta-learning. However, results show that the fine-tuning step makes marginal
improvements. In this paper, 1) we figure out the reason, i.e., in the
pre-trained feature space, the base classes already form compact clusters while
novel classes spread as groups with large variances, which implies that
fine-tuning feature extractor is less meaningful; 2) instead of fine-tuning
feature extractor, we focus on estimating more representative prototypes.
Consequently, we propose a novel prototype completion based meta-learning
framework. This framework first introduces primitive knowledge (i.e.,
class-level part or attribute annotations) and extracts representative features
for seen attributes as priors. Second, a part/attribute transfer network is
designed to learn to infer the representative features for unseen attributes as
supplementary priors. Finally, a prototype completion network is devised to
learn to complete prototypes with these priors. Moreover, to avoid the
prototype completion error, we further develop a Gaussian based prototype
fusion strategy that fuses the mean-based and completed prototypes by
exploiting the unlabeled samples. Extensive experiments show that our method:
(i) obtains more accurate prototypes; (ii) achieves superior performance on
both inductive and transductive FSL settings.

本论文提出了一种基于原型补全的元学习框架，采用属性转移网络，利用类部件或属性的先验来学习预测看不见部分的属性，然后利用高斯原型融合策略来完成原型。该方法在归纳式和传递式少样本学习的情境中均取得了优越的性能。

Few-Shot 学习的原型完备性

Prototype Completion for Few-Shot Learning

Few-shot learning is a challenging task, which aims to learn a classifier for
novel classes with few examples. Pre-training based meta-learning methods
effectively tackle the problem by pre-training a feature extractor and then
fine-tuning it through the nearest centroid based meta-learning. However,
results show that the fine-tuning step makes very marginal improvements. In
this paper, 1) we figure out the key reason, i.e., in the pre-trained feature
space, the base classes already form compact clusters while novel classes
spread as groups with large variances, which implies that fine-tuning the
feature extractor is less meaningful; 2) instead of fine-tuning the feature
extractor, we focus on estimating more representative prototypes during
meta-learning. Consequently, we propose a novel prototype completion based
meta-learning framework. This framework first introduces primitive knowledge
(i.e., class-level part or attribute annotations) and extracts representative
attribute features as priors. Then, we design a prototype completion network to
learn to complete prototypes with these priors. To avoid the prototype
completion error caused by primitive knowledge noises or class differences, we
further develop a Gaussian based prototype fusion strategy that combines the
mean-based and completed prototypes by exploiting the unlabeled samples.
Extensive experiments show that our method: (i) can obtain more accurate
prototypes; (ii) outperforms state-of-the-art techniques by 2% - 9% in terms of
classification accuracy. Our code is available online.

本研究提出了一种新的基于原型完成的元学习框架，该框架设计了一个原型完成网络来学习完成原型，通过高斯原型融合策略进一步提高分类器精度，实现了对于小样本分类的更准确的原型对比，并且在分类准确度上领先于现有的技术 2％-9％。