The content explores the effectiveness of pre-training models to improve robustness in distribution shifts. It discusses how pre-training can address poor extrapolation but not dataset biases, providing insights into developing more robust models through a combination of pre-training and bias-handling interventions.
The study delves into the failure modes that pre-training can and cannot address, emphasizing the importance of understanding when pre-training is beneficial. It highlights the implications for developing robust models by combining pre-training with interventions designed to prevent exploiting biases.
Furthermore, the content examines the empirical robustness benefits of pre-training under different types of shifts, showcasing how pre-trained models exhibit effective robustness on out-of-support shifts but not on in-support shifts. It also explores the strategy of curating datasets for fine-tuning, demonstrating how a small, non-diverse de-biased dataset can lead to significantly more robust models than training from scratch on a large and diverse but biased dataset.
Overall, the content provides valuable insights into leveraging pre-training for improving model robustness in distribution shifts and emphasizes the importance of considering specific failure modes to enhance model performance.
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arxiv.org
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by Benjamin Coh... at arxiv.org 03-04-2024
https://arxiv.org/pdf/2403.00194.pdfDeeper Inquiries