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.
For a quick overview of our project, visit our website.
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]