id data
Long-Tailed Out-of-Distribution Detection via Normalized Outlier Distribution Adaptation
One key challenge in Out-of-Distribution (OOD) detection is the absence of ground-truth OOD samples during training.
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Self-Calibrated Tuning of Vision-Language Models for Out-of-Distribution Detection Geng Y u
The code is publicly available at: https://github.com/tmlr-group/SCT .
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GradOrth: A Simple yet Efficient Out-of-Distribution Detection with Orthogonal Projection of Gradients
Detecting out-of-distribution (OOD) data is crucial for ensuring the safe deployment of machine learning models in real-world applications.
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25cc3adf8c85f7c70989cb8a97a691a7-Paper-Conference.pdf
We reveal an interestingphenomenontermed confidencemutation,wheretheconfidenceofOOD data can decrease significantly under the corruptions, while the ID data shows a higher confidence expectation considering the resistance of semantic features.
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