# Memory-Augmented Relation Network for Few-Shot Learning

arXiv: https://arxiv.org/abs/2005.04414 · HTML: https://mlsanity.com/paper/2005.04414/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2020.

## Built on by (most cited first, 10 of 10)

- [Meta-learning approaches for few-shot learning: A survey of recent advances](https://mlsanity.com/paper/2303.07502/) (2023)
- [Cross-view Geo-localization with Evolving Transformer](https://mlsanity.com/paper/2107.00842/) (2021)
- [Revisiting Local Descriptor for Improved Few-Shot Classification](https://mlsanity.com/paper/2103.16009/) (2021)
- [Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-Learning](https://mlsanity.com/paper/2108.06647/) (2021)
- [Few-shot Partial Multi-view Learning](https://mlsanity.com/paper/2105.02046/) (2021)
- [Semantic-Based Few-Shot Learning by Interactive Psychometric Testing](https://mlsanity.com/paper/2112.09201/) (2021)
- [Few-shot Learning with Global Relatedness Decoupled-Distillation](https://mlsanity.com/paper/2107.05583/) (2021)
- [Memory-guided Network with Uncertainty-based Feature Augmentation for Few-shot Semantic Segmentation](https://mlsanity.com/paper/2406.00545/) (2024)
- [Noise-Tolerant Hybrid Prototypical Learning with Noisy Web Data](https://mlsanity.com/paper/2501.02476/) (2025)
- [Few-Shot Learning for Industrial Time Series: A Comparative Analysis Using the Example of Screw-Fastening Process Monitoring](https://mlsanity.com/paper/2506.13909/) (2025)

## Abstract (arXiv)

Metric-based few-shot learning methods concentrate on learning transferable feature embedding that generalizes well from seen categories to unseen categories under the supervision of limited number of labelled instances. However, most of them treat each individual instance in the working context separately without considering its relationships with the others. In this work, we investigate a new metric-learning method, Memory-Augmented Relation Network (MRN), to explicitly exploit these relationships. In particular, for an instance, we choose the samples that are visually similar from the working context, and perform weighted information propagation to attentively aggregate helpful information from the chosen ones to enhance its representation. In MRN, we also formulate the distance metric as a learnable relation module which learns to compare for similarity measurement, and augment the working context with memory slots, both contributing to its generality. We empirically demonstrate that MRN yields significant improvement over its ancestor and achieves competitive or even better performance when compared with other few-shot learning approaches on the two major benchmark datasets, i.e. miniImagenet and tieredImagenet.