Kthyeon/FINE_official
NeurIPS 2021, "Fine Samples for Learning with Noisy Labels"
This project helps machine learning engineers and researchers improve the accuracy of their AI models when training data contains incorrect labels. It takes a dataset with potentially noisy labels and applies a method called FINE to select the most reliable samples. The outcome is a more robust AI model that performs better even with imperfect input data.
No commits in the last 6 months.
Use this if you are building an image classification or similar AI model and suspect your training data labels are not perfectly accurate, leading to suboptimal model performance.
Not ideal if your primary concern is managing extremely large datasets efficiently or if you are looking for a solution that doesn't involve custom Python script execution.
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Python
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Last pushed
Nov 29, 2021
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