AI & Machine Learning

Inside-Out: Measuring Generalization in Vision Transformers Through Inner Workings

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Researchers have developed new label-free proxy metrics to evaluate the generalization performance of vision transformers, addressing a critical need in high-stakes applications where labeled data are scarce. These metrics, Dependency Depth Bias and Circuit Shift Score, focus on internal model mechanisms rather than just output accuracy, offering more reliable predictions before and after deployment. This advancement improves the selection and monitoring of models under distribution shifts by an average of 13.4% and 34.1%, respectively, compared to existing methods.

Read the full article at arXiv cs.LG (ML)

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