A new deep hierarchical knowledge loss (DHK) with tree and triplet components improves fault intensity diagnosis by modeling class hierarchies on industrial datasets.
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GenHAR generalizes cross-domain human activity recognition by 9.97% accuracy and 6.4x lower FLOPs via tokenized sensor data, frequency channel correlations, selective masking, and efficient attention, with deployment detecting 2.15 billion activities.
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Deep Hierarchical Knowledge Loss for Fault Intensity Diagnosis
A new deep hierarchical knowledge loss (DHK) with tree and triplet components improves fault intensity diagnosis by modeling class hierarchies on industrial datasets.
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GenHAR: Generalizing Cross-domain Human Activity Recognition for Last-mile Delivery
GenHAR generalizes cross-domain human activity recognition by 9.97% accuracy and 6.4x lower FLOPs via tokenized sensor data, frequency channel correlations, selective masking, and efficient attention, with deployment detecting 2.15 billion activities.