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16 hours ago · By analyzing and further modeling the real-world abnormal data, a criterion for synthesizing abnormal data is proposed to augment the scale of abnormal data.
7 days ago · In this study, we investigate the detection of distribution shifts, with a focus on exploring and analyzing OOD detection and OSR methods and benchmarks. Our ...
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4 days ago · Detecting test-time distribution shift has emerged as a key capability for safely deployed machine learning models, with the question being tackled under ...
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5 days ago · VisionGPT: LLM-Assisted Real-Time Anomaly Detection for Safe Visual ... A Comparative Analysis of CNN-based Deep Learning Models for Landslide Detection ...
3 days ago · This study aims to propose an improved GAN, incorporating temporal and spatial features, for the augmentation of continuous highway compaction detection metrics ...
7 days ago · The study finds that downstream predictor variables offer the highest predictability, with downstream tropical depression (TD)-type wave-based predictors being ...
7 days ago · Out-of-distribution (OOD) detection is crucial to modern deep learning applications by identifying and alerting about the OOD samples that should not be ...
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4 days ago · ... Time Series Data from Public Sources. 2024-08-31, ANSM5, Functions and Data for the Book "Applied Nonparametric Statistical Methods", 5th Edition. 2024-08-31 ...
7 hours ago · Date, Package, Title. 2024-09-05, AIFFtools, Read AIFF Files and Convert to WAVE Format. 2024-09-05, CALANGO, Comparative Analysis with Annotation-Based ...
6 days ago · DCGAN processes image data through deep convolutional networks to learn the distributional properties of images and extract key visual features effectively.