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Found 3 papers, 1 papers with code
A Rigorous Evaluation of Real-World Distribution Shifts
Motivated by this, we introduce a new data augmentation method which advances the state-of-the-art and outperforms models pretrained with 1000x more labeled data.
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
We find that using larger models and artificial data augmentations can improve robustness on real-world distribution shifts, contrary to claims in prior work.
Ranked #29 on
Domain Generalization
on ImageNet-R
Healthcare NER Models Using Language Model Pretraining
Our solution uses a combination of Natural Language Processing (NLP) techniques and a web-based annotation tool to optimize the performance of a custom Named Entity Recognition (NER) [1] model trained on a limited amount of EHR training data.
