Update README.md

README.md

@@ -1,7 +1,17 @@
-# FSL_Subspace
+# Few-shot Learning Using Random Subspace
 
-Codes for work on few-shot learning on chest x-ray images ([paper](https://openreview.net/pdf?id=AF97JZpgPe)).
+## 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).
 
-Check our [website](https://few-shot-learning-on-chest-x-ray.github.io/Project-Page/) for a brief summary of the paper.
+For a quick overview of our project, visit our [website](https://few-shot-learning-on-chest-x-ray.github.io/Project-Page/).
 
-tl;dr : We propose a computationally efficient few-shot learning method for diagnosing chest X-rays, which uses an ensemble of random subspaces and a novel loss function to create well-separated clusters of training data in discriminative subspaces. Our method is almost 1.8 times faster than the popular t-SVD method for subspace decomposition and yields promising results on large-scale CXR datasets.
+## 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]