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FOMO-Shift: self-supervised distribution matching for safe deployment of AI foundation models

Funder: Netherlands Organisation for Scientific Research (NWO)Project code: NGF.1609.242.045
Funded under: NGF - AiNed AiNed XS Europa 2024-2

FOMO-Shift: self-supervised distribution matching for safe deployment of AI foundation models

Description

The reliability and accuracy of artificial intelligence (AI) algorithms can degrade when such algorithms are applied to new, varied datasets — a common challenge known as “distribution shift”. For example, an AI model trained to detect tumors in MRI scans from a single hospital may not perform well on scans from another hospital. To address this issue, we aim to adapt the internal representations of “foundation models”, which are pre-trained on diverse data without specific labels. These adapted representations may allow us to partially mitigate distribution shift and boost the model’s performance in a broader environment.

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