# Are Fewer Labels Possible for Few-shot Learning?

arXiv: https://arxiv.org/abs/2012.05899 · HTML: https://mlsanity.com/paper/2012.05899/

## Key facts

- Cited by 2 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 10 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [Enabling the Network to Surf the Internet](https://mlsanity.com/paper/2102.12205/) (2021)
- [SpurAudio: A Benchmark for Studying Shortcut Learning in Few-Shot Audio Classification](https://mlsanity.com/paper/2605.13672/) (2026)

## Abstract (arXiv)

Few-shot learning is challenging due to its very limited data and labels. Recent studies in big transfer (BiT) show that few-shot learning can greatly benefit from pretraining on large scale labeled dataset in a different domain. This paper asks a more challenging question: "can we use as few as possible labels for few-shot learning in both pretraining (with no labels) and fine-tuning (with fewer labels)?". Our key insight is that the clustering of target samples in the feature space is all we need for few-shot finetuning. It explains why the vanilla unsupervised pretraining (poor clustering) is worse than the supervised one. In this paper, we propose transductive unsupervised pretraining that achieves a better clustering by involving target data even though its amount is very limited. The improved clustering result is of great value for identifying the most representative samples ("eigen-samples") for users to label, and in return, continued finetuning with the labeled eigen-samples further improves the clustering. Thus, we propose eigen-finetuning to enable fewer shot learning by leveraging the co-evolution of clustering and eigen-samples in the finetuning. We conduct experiments on 10 different few-shot target datasets, and our average few-shot performance outperforms both vanilla inductive unsupervised transfer and supervised transfer by a large margin. For instance, when each target category only has 10 labeled samples, the mean accuracy gain over the above two baselines is 9.2% and 3.42 respectively.