yfzhang114/Environment-Label-Smoothing
This is an official PyTorch implementation of the ICLR 2023 paper 《Free Lunch for Domain Adversarial Training: Environment Label Smoothing》.
This project helps machine learning engineers improve the accuracy and reliability of their models when applied to new, unseen datasets. It takes existing domain adversarial training models and applies a "label smoothing" trick to produce models that generalize better across different data environments. Data scientists and machine learning researchers dealing with real-world data variability will find this useful.
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Use this if your machine learning models struggle to perform well when deployed on data that is slightly different from your training data, causing issues like inconsistent predictions or poor generalization.
Not ideal if you are not already using or planning to use domain adversarial training methods, as this method specifically enhances that approach.
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Feb 04, 2023
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