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

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Last update: Jun 05, 2022

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Deep Learning Est-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

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