# RNNP: A Robust Few-Shot Learning Approach

arXiv: https://arxiv.org/abs/2011.11067 · HTML: https://mlsanity.com/paper/2011.11067/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2020.

## Built on by (most cited first, 9 of 9)

- [Few-shot Learning with Noisy Labels](https://mlsanity.com/paper/2204.05494/) (2022)
- [DETA: Denoised Task Adaptation for Few-Shot Learning](https://mlsanity.com/paper/2303.06315/) (2023)
- [Towards Few-Shot Learning in the Open World: A Review and Beyond](https://mlsanity.com/paper/2408.09722/) (2024)
- [Towards Robust Few-shot Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2309.11228/) (2023)
- [Transformer-Powered Surrogates Close the ICF Simulation-Experiment Gap with Extremely Limited Data](https://mlsanity.com/paper/2312.03642/) (2023)
- [Data-Efficient and Robust Task Selection for Meta-Learning](https://mlsanity.com/paper/2405.07083/) (2024)
- [BatMan-CLR: Making Few-shots Meta-Learners Resilient Against Label Noise](https://mlsanity.com/paper/2309.06046/) (2023)
- [CRoF: CLIP-based Robust Few-shot Learning on Noisy Labels](https://mlsanity.com/paper/2412.12793/) (2024)
- [RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels](https://mlsanity.com/paper/2506.03461/) (2025)

## Abstract (arXiv)

Learning from a few examples is an important practical aspect of training classifiers. Various works have examined this aspect quite well. However, all existing approaches assume that the few examples provided are always correctly labeled. This is a strong assumption, especially if one considers the current techniques for labeling using crowd-based labeling services. We address this issue by proposing a novel robust few-shot learning approach. Our method relies on generating robust prototypes from a set of few examples. Specifically, our method refines the class prototypes by producing hybrid features from the support examples of each class. The refined prototypes help to classify the query images better. Our method can replace the evaluation phase of any few-shot learning method that uses a nearest neighbor prototype-based evaluation procedure to make them robust. We evaluate our method on standard mini-ImageNet and tiered-ImageNet datasets. We perform experiments with various label corruption rates in the support examples of the few-shot classes. We obtain significant improvement over widely used few-shot learning methods that suffer significant performance degeneration in the presence of label noise. We finally provide extensive ablation experiments to validate our method.