RADAR produces anomaly maps directly from attention-based diffusion models in a single forward pass, achieving higher F1 scores than reconstruction-based diffusion and statistical baselines on MVTec-AD and 3D-printed material data.
Generative adversarial nets,
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LLM-AUG applies LLM in-context learning for embedding-space data augmentation in wireless ML, outperforming baselines and reaching near-oracle accuracy with only 15% labeled data on RadioML and IC datasets.
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Single-Step Reconstruction-Free Anomaly Detection and Segmentation via Diffusion Models
RADAR produces anomaly maps directly from attention-based diffusion models in a single forward pass, achieving higher F1 scores than reconstruction-based diffusion and statistical baselines on MVTec-AD and 3D-printed material data.
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LLM-AUG: Robust Wireless Data Augmentation with In-Context Learning in Large Language Models
LLM-AUG applies LLM in-context learning for embedding-space data augmentation in wireless ML, outperforming baselines and reaching near-oracle accuracy with only 15% labeled data on RadioML and IC datasets.