LiZAD reduces memory by 61.5%, parameters by 74.6%, and latency by 3.02x versus six prior ZSAD models while incurring only a 6.4% average P-AUROC drop on VisA, BTAD, MPDD, and MVTec-AD, with successful Jetson deployment.
TinyGLASS: Real-Time Self-Supervised In-Sensor Anomaly Detection,
2 Pith papers cite this work. Polarity classification is still indexing.
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TinyML models on IMX500 deliver 96.68% accuracy on EuroSAT at 17.4 FPS and 14.19 mJ per inference within 8 MB memory for in-sensor EO.
citing papers explorer
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LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing
LiZAD reduces memory by 61.5%, parameters by 74.6%, and latency by 3.02x versus six prior ZSAD models while incurring only a 6.4% average P-AUROC drop on VisA, BTAD, MPDD, and MVTec-AD, with successful Jetson deployment.
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Exploiting In-Sensor Computing for Energy-Efficient Earth Observation
TinyML models on IMX500 deliver 96.68% accuracy on EuroSAT at 17.4 FPS and 14.19 mJ per inference within 8 MB memory for in-sensor EO.