# Finding Significant Features for Few-Shot Learning using Dimensionality Reduction

arXiv: https://arxiv.org/abs/2107.06992 · HTML: https://mlsanity.com/paper/2107.06992/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 6 Jul 2021.

## Builds on (most cited first, 14 of 14)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [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)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [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)
- [Learning to learn by gradient descent by gradient descent](https://mlsanity.com/paper/1606.04474/) (2016)
- [Meta-Learning with Differentiable Convex Optimization](https://mlsanity.com/paper/1904.03758/) (2019)
- [Learning to Optimize](https://mlsanity.com/paper/1606.01885/) (2016)
- [Finding Task-Relevant Features for Few-Shot Learning by Category Traversal](https://mlsanity.com/paper/1905.11116/) (2019)
- [Self-Supervised Learning For Few-Shot Image Classification](https://mlsanity.com/paper/1911.06045/) (2019)
- [Revisiting Metric Learning for Few-Shot Image Classification](https://mlsanity.com/paper/1907.03123/) (2019)
- [Multi-scale Adaptive Task Attention Network for Few-Shot Learning](https://mlsanity.com/paper/2011.14479/) (2020)
- [Rehabilitating Isomap: Euclidean Representation of Geodesic Structure](https://mlsanity.com/paper/2006.10858/) (2020)

## Built on by (most cited first, 1 of 1)

- [Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning](https://mlsanity.com/paper/2210.13654/) (2022)

## Abstract (arXiv)

Few-shot learning is a relatively new technique that specializes in problems where we have little amounts of data. The goal of these methods is to classify categories that have not been seen before with just a handful of samples. Recent approaches, such as metric learning, adopt the meta-learning strategy in which we have episodic tasks conformed by support (training) data and query (test) data. Metric learning methods have demonstrated that simple models can achieve good performance by learning a similarity function to compare the support and the query data. However, the feature space learned by a given metric learning approach may not exploit the information given by a specific few-shot task. In this work, we explore the use of dimension reduction techniques as a way to find task-significant features helping to make better predictions. We measure the performance of the reduced features by assigning a score based on the intra-class and inter-class distance, and selecting a feature reduction method in which instances of different classes are far away and instances of the same class are close. This module helps to improve the accuracy performance by allowing the similarity function, given by the metric learning method, to have more discriminative features for the classification. Our method outperforms the metric learning baselines in the miniImageNet dataset by around 2% in accuracy performance.