Meta-learning has become a practical approach towards few-shot image
classification, where "a strategy to learn a classifier" is meta-learned on
labeled base classes and can be applied to tasks with novel classes. We remove
the requirement of base class labels and learn generalizable embeddings via
Unsupervised Meta-Learning (UML). Specifically, episodes of tasks are
constructed with data augmentations from unlabeled base classes during
meta-training, and we apply embedding-based classifiers to novel tasks with
labeled few-shot examples during meta-test. We observe two elements play
important roles in UML, i.e., the way to sample tasks and measure similarities
between instances. Thus we obtain a strong baseline with two simple
modifications -- a sufficient sampling strategy constructing multiple tasks per
episode efficiently together with a semi-normalized similarity. We then take
advantage of the characteristics of tasks from two directions to get further
improvements. First, synthesized confusing instances are incorporated to help
extract more discriminative embeddings. Second, we utilize an additional
task-specific embedding transformation as an auxiliary component during
meta-training to promote the generalization ability of the pre-adapted
embeddings. Experiments on few-shot learning benchmarks verify that our
approaches outperform previous UML methods and achieve comparable or even
better performance than its supervised variants.

本篇论文提出了基于无监督元学习（UML）的通用嵌入向量生成方式用于 few-shot learning，并结合数据增强、半标准化相似度、混淆实例合成、任务相关嵌入变换等技术，取得了显著优于以前 UML 方法和甚至优于有监督方法的成果。