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Showing 1 to 4 of 4 for “"Out-of-Distribution (OOD) detection"”.
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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
… their own limitations? A crucial aspect of robustness is the ability to identify when an input falls outside the scope of one’s knowledge or training --- a task known as out-of-distribution (OOD) detection. For example, a dog breed classifier should ideally recognize a cat image as OOD …
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On Impact of Network Architecture for Deep Learning
<p>The architecture of neural networks is a crucial factor in the success of deep learning models across a range of fields, including computer vision and natural language processing (NLP). Specific architectures are tailored to address particular tasks, and the selection of architecture can …
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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 …