PyTorch implementation of the paper: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features

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Deep LearningEst-T-MI
Overview

Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features

Estimate the noise transition matrix with f-mutual information.

This code is a PyTorch implementation of the paper:

Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features [paper]

Requirements

  • Python3
  • Pytorch
  • Pandas
  • Numpy
  • Scipy
  • Sklearn

Quick Run on UCI datasets:

nohup bash ./run.sh > result.log &

Get results:

cat result.log | grep "Error"

NLP benchmarks:

  • Prepare BERT embeddings
  • Change DataLoader to TextDataLoader
  • Use the corresponding dataset name and run (more details will be available soon)
Owner
[email protected]
REsponsible & Accountable Learning (REAL) @ University of California, Santa Cruz
<a href=[email protected]">
GitHub Repository
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