Von Mises-Fisher Mixture Model with Dynamic Shrinkage for Realistic Test-Time Transduction
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TL;DR - MOON is a training-free, model-agnostic test-time transduction method for vision-language models that remains robust under imbalanced class distributions. It uses dynamically adjusted shrinkage toward zero-shot priors to reduce unreliable assignments and negative transfer.
- Reframes transduction as penalized likelihood estimation with KL-divergence anchoring.
- Models normalized feature representations using a mixture of von Mises-Fisher distributions.
- Adjusts shrinkage strength dynamically at both instance and class levels.
- Requires neither task-specific hyperparameter tuning nor additional training.
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Von Mises-Fisher Mixture Model with Dynamic Shrinkage for Realistic Test-Time Transduction
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TL;DR - MOON is a training-free, model-agnostic test-time transduction method for vision-language models that remains robust under imbalanced class distributions. It uses dynamically adjusted shrinkage toward zero-shot priors to reduce unreliable assignments and negative transfer.
- Reframes transduction as penalized likelihood estimation with KL-divergence anchoring.
- Models normalized feature representations using a mixture of von Mises-Fisher distributions.
- Adjusts shrinkage strength dynamically at both instance and class levels.
- Requires neither task-specific hyperparameter tuning nor additional training.