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Showing 1 to 7 of 7 for “"Ood Detection"”.
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Improved Out-of-Distribution Detection Using Segmented Images and Prompt-Only Text Reasoning
The problem of Out-of-Distribution (OOD) detection has been thoroughly researched but continues to underperform in "near-OOD" settings, where OOD data may be very similar or inseparable from in-distribution (ID) data. The problem is that current state-of-the-art OOD detection methods fail to learn …
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Neighborhood Transformation Marginalization forOOD Detection
Out-of-distribution (OOD) detection is an important part of enabling the real world deployment of machine learning models. Many recent methods developed to perform OOD detection rely on calculating a score function on a given test point then thresholding the value to classify the point as …
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Anomalous Inputs in Deep Learning: a Probabilistic Perspective
… --- a task known as out-of-distribution (OOD) detection. For example, a dog breed classifier should ideally recognize a cat image as OOD and refrain from classifying it as a breed of dog. Conversely, can we manipulate neural networks into making confident but incorrect classifications? …
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On Impact of Network Architecture for Deep Learning
… of CNN architectures on Out-of-Distribution (OOD) detection tasks using Neural Architecture Search (NAS). To improve the quality of evaluation on OOD detection during the search, we propose evolving distillation based on our multi-view feature learning explanation. Experimental results show …
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Statistical Methods for Out-of-distribution Detection
… test samples could be out-of-distribution (OOD) that are drawn from distributions different from that of ID samples. Accordingly, OOD detection aims to identify OOD samples in test phases. The main challenge lies in that a network could provide high-confidence predictions for OOD samples, …
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Secure and reliable deep learning in signal processing
… which we refer to as ``out-of-distribution (OOD)'' data. Failing to detect OOD testing data may expose serious security risks. Third, deep learning algorithms can be easily fooled when the input data are falsified. Such vulnerabilities may cause severe risks in safety-critical applications …
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Learning structured representations with hyperbolic embeddings
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01