# Interpretable Concept-based Prototypical Networks for Few-Shot Learning

arXiv: https://arxiv.org/abs/2202.13474 · HTML: https://mlsanity.com/paper/2202.13474/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 14 references that are in the map.
- First posted on arXiv 27 Feb 2022.

## Builds on (most cited first, 10 of 10)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [A Closer Look at Few-shot Classification](https://mlsanity.com/paper/1904.04232/) (2019)
- [Meta-Learning with Latent Embedding Optimization](https://mlsanity.com/paper/1807.05960/) (2018)
- [Meta-Learning with Differentiable Convex Optimization](https://mlsanity.com/paper/1904.03758/) (2019)
- [How to train your MAML](https://mlsanity.com/paper/1810.09502/) (2018)
- [Relational Embedding for Few-Shot Classification](https://mlsanity.com/paper/2108.09666/) (2021)

## Built on by (most cited first, 4 of 4)

- [Focal Modulation Networks for Interpretable Sound Classification](https://mlsanity.com/paper/2402.02754/) (2024)
- [Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learning](https://mlsanity.com/paper/2203.16464/) (2022)
- [Interpretable Few-shot Learning with Online Attribute Selection](https://mlsanity.com/paper/2211.09107/) (2022)
- [Predicting Thermoelectric Power Factor of Bismuth Telluride During Laser Powder Bed Fusion Additive Manufacturing](https://mlsanity.com/paper/2303.15663/) (2023)

## Abstract (arXiv)

Few-shot learning aims at recognizing new instances from classes with limited samples. This challenging task is usually alleviated by performing meta-learning on similar tasks. However, the resulting models are black-boxes. There has been growing concerns about deploying black-box machine learning models and FSL is not an exception in this regard. In this paper, we propose a method for FSL based on a set of human-interpretable concepts. It constructs a set of metric spaces associated with the concepts and classifies samples of novel classes by aggregating concept-specific decisions. The proposed method does not require concept annotations for query samples. This interpretable method achieved results on a par with six previously state-of-the-art black-box FSL methods on the CUB fine-grained bird classification dataset.