Dec 8, 2020 · This paper presents a principled approach for detecting out-of-distribution (OOD) samples in deep neural networks (DNN).
This paper presents a principled approach for detecting out-of-distribution (OOD) samples in deep neural networks (DNN). Modeling probability distributions ...
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FRE: A Fast Method For Anomaly Detection And Segmentation · Out-Of-Distribution Detection With Subspace Techniques And Probabilistic Modeling Of Features.
Detecting out-of-distribution (OOD) data is crucial for ensuring the safe deploy- ment of machine learning models in real-world applications.
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Out-Of-Distribution Detection With Subspace Techniques And Probabilistic Modeling Of Features · I. NdiourNilesh A. AhujaOmesh Tickoo. Computer Science. ArXiv.
Out-of-distribution detection with subspace techniques and probabilistic modeling of features. arXiv preprint. arXiv:2012.04250, 2020. 2. [25] Yuval Netzer ...
This paper presents a fast, principled approach for detecting anomalous and out-of-distribution (OOD) samples in deep neural networks (DNN).
We present a low-complexity approach for detecting out-of-distribution (OOD) and adversarial samples in deep neural networks. We model the outputs of the ...
Out-of-distribution (OOD) detection is an important task to ensure the reliability and safety of deep learning and the discriminator models outperform ...