Abstract: Product surface defect detection is a crucial technology in industrial production. The adoption of deep learning-based algorithms for inspecting product surface defects has been steadily ...
By 2050, urban centers will house nearly 70% of the global population. Transitioning to localized food production via Urban Agriculture (UA) including ...
Abstract: We propose a memory-augmented deep learning model for semisupervised anomaly detection (AD). While many traditional AD methods focus on modeling the distribution of normal data, additional ...
AI's role in data centers enhances operational efficiency, predictive maintenance, and cybersecurity, paving the way for ...
Fraudulent Accounts Target Claude AI In a surprising turn of events, Anthropic has disclosed a significant security breach involving around 25,000 fraudulent accounts that were used to probe its ...
Accurate RNA splicing is essential for gene expression and human health, yet predicting how DNA sequence variations affect ...
Modern medical imaging increasingly relies on artificial intelligence to support detection, diagnosis, and prognostic ...
Humans have been successfully trained to spot AI-generated faces in a study led by researchers at the Australian National ...
A new study explores deep learning for image-based defect detection during 3D printing, looking to catch bad builds.
This is the implementation of PatchAD: A Lightweight Patch-based MLP-Mixer for Time Series Anomaly Detection.
Leaders at EBRAINS, a collection of tools and data resources for basic neuroscience research, are finalizing the platform’s 10-year plan amid calls to improve its usability.
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