Optimized Lightweight Face Recognition Network Improves Performance on Embedded AI Platforms
Researchers have developed an improved MobileFaceNet model that balances accuracy and efficiency on embedded artificial intelligence platforms.
Researchers have developed an improved MobileFaceNet model that balances accuracy and efficiency on embedded artificial intelligence platforms.
Researchers developed a multi-stage semantic fusion framework integrating contrastive learning, multi-head attention, and knowledge distillation to improve cross-language summarization accuracy ...
A comprehensive review analyzes how hardware innovations, traditional signal processing, and advanced deep learning algorithms overcome severe noise limitations in ...
Researchers developed a compressed reinforcement learning controller deployed on a microcontroller that maintained an 83 percent time-in-range in simulated type ...
Researchers have developed a multi-view collaborative variational autoencoder that leverages knowledge distillation transfer learning to predict lncRNA associations in cervical ...
A comprehensive critical review reveals that over two-thirds of recent lightweight security mechanisms for resource-constrained networks rely solely on theoretical ...
A study demonstrates that federated learning models for ICU mortality prediction preserve feature explainability consistency and high predictive performance compared ...
A cross-modal artificial intelligence framework accurately recommended initial positive airway pressure settings for obstructive sleep apnea across a multicenter retrospective ...
Researchers developed a dual-teacher knowledge distillation framework that fuses unstructured clinical notes with vital signs to improve early sepsis prediction ...
A lightweight detector named GFLNet improves mAP@0.5 over the baseline on the DIOR and UCAS-AOD datasets.
© 2026 Scienmag - Science Magazine
© 2026 Scienmag - Science Magazine