amazon-science/learning_under_noisy_labels

amazon-science/learning_under_noisy_labels

Language: Python

License: Apache-2.0

Stars: 2

Forks: 0

Open issues: 0

Created: 2026-02-17T06:46:23Z

Pushed: 2026-02-17T14:45:24Z

Default branch: main

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README:

When Annotators Disagree: A Principled Approach to Learning with Noisy Labels

The code is written in Python 3.

Dependencies

Install the Python3 dependecies by executing the following command:

pip3 install -r requirements.txt

Tests

In the root folder you can run some sanity check tests, by executing the following command:

bash run_tests.sh

To run Text Classification experiments:

cd examples/text_experiments

bash run_text_exp.sh

To run TrashNet experiments:

cd data && git clone https://github.com/garythung/trashnet.git

mv trashnet/data/dataset-resized.zip . && rm -rf trashnet && unzip dataset-resized.zip

cd ../examples/trashnet_experiments && python3 generate_synthetic_annotations.py

bash run_trashnet_exp.sh

To run experiments on CIFAR-10N:

cd examples/cifar10n_experiments

bash run_cifar_exp.sh

To run synthetic experiments:

cd examples/syntethic_experiments

bash run_exp.sh

For doubts or errors feel free to ping purificato@diag.uniroma1.it!

Security

See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information.

License

This library is licensed under the Apache 2.0 License.

Acknowledgments

The implementation of Dawid-Skene and Iterative-Weighted Majority Voting draws from thethe paper A Lightweight, Effective, and Efficient Model for LabelAggregation in Crowdsourcing. We gratefully acknowledge the authors for making their code available.