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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]

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Dataset used to train darklord25/fewshot_random_subspace