Perturb-and-Correct generates epistemically diverse predictors from a single pretrained network via hidden-layer perturbations followed by affine least-squares corrections that enforce agreement on calibration data.
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Energy-based fine-tuning outperforms other OOD detection methods on the real-world Plant Pathology 2021 dataset, improving detection over softmax while maintaining in-distribution accuracy.
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Perturb and Correct: Post-Hoc Ensembles using Affine Redundancy
Perturb-and-Correct generates epistemically diverse predictors from a single pretrained network via hidden-layer perturbations followed by affine least-squares corrections that enforce agreement on calibration data.
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Beyond Toy Benchmarks: A Systematic Evaluation of OOD Detection Methods For Plant Pathology Classification
Energy-based fine-tuning outperforms other OOD detection methods on the real-world Plant Pathology 2021 dataset, improving detection over softmax while maintaining in-distribution accuracy.