Robust Model Generalization
The discussion centers on enhancing model robustness, particularly in medical imaging systems deployed in unfamiliar hospitals. By leveraging hospital labels during training, models can be made invariant to differences between hospitals, improving generalization. Additionally, the potential for continuous training during testing is explored, allowing robots to adapt and learn from new data as they encounter it.In this clip
From this podcast
The Robot Brains Podcast
S3 E2 Stanford Prof Chelsea Finn: How to build AI that can keep up with an always changing world
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