# Few-shot Learning Using Random Subspace

## Overview
This repository contains the code for our work on few-shot learning for chest X-ray images. Our approach is detailed in our paper, which can be accessed [here](https://openreview.net/pdf?id=AF97JZpgPe).

For a quick overview of our project, visit our [website](https://few-shot-learning-on-chest-x-ray.github.io/Project-Page/).

## Project Summary
Our project presents a novel method for few-shot learning, specifically tailored for the analysis of chest X-ray (CXR) images. The key features of our method include:

- **Efficiency**: Our approach is nearly 1.8 times faster than the traditional t-SVD method for subspace decomposition.
- **Effective Clustering**: The method ensures the creation of well-separated clusters of training data in discriminative subspaces.
- **Promising Results**: We have tested our method on large-scale CXR datasets, yielding encouraging outcomes.


## Contact
Reach out to the authors [details provided in the project page]
{"language": ["en"], "license": "mit", "tags": ["Few-Shot Learning", "medical", "Computer Vision", "Image Classification"], "datasets": ["alkzar90/NIH-Chest-X-ray-dataset"], "metrics": ["f1", "accuracy"], "pipeline_tag": "image-classification"}
Few-Shot Learning medical Computer Vision Image Classification image-classification en dataset:alkzar90/NIH-Chest-X-ray-dataset license:mit region:us