Deep vision models often degrade under distribution shift. Test-time adaptation can improve robustness but typically requires iterative optimization, hyperparameter tuning, and multiple forward-backward passes. We propose Zero-Training Fisher Geometry Alignment (ZFGA), a closed-form method that improves robustness unde...
Behraj Khan, T. Syed, Syed Ahmad Chan Bukhari· 0 citations
Covariate shift across training-data partitions biases model selection and parameter estimation in cross-validation, lifelong learning, and federated learning. We propose \textit{Partition-Induced Covariate-shift Correction} (\texttt{PIcsC}), a Fisher information-based regularization framework that mitigates distributi...
Behraj Khan, Behroz Mirza, S. Bukhari et al.· arXiv.org· 0 citations
The combined objective is derived, which proof steps transfer from the classification setting without modification and which require adaptation, and which require adaptation on the PhysioNet 2019 Sepsis Challenge, treating the two hospital systems as sequential training fragments and the unseen system as an out-of-dist...
Behraj Khan, Shabir Ahmad, S. Bukhari et al.· arXiv.org· 1 citation
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