Nokia Technnologies Oy (20240303486). Collaborative Online Model Adaptation For Resource Constraint Devices simplified abstract
Contents
Collaborative Online Model Adaptation For Resource Constraint Devices
Organization Name
Inventor(s)
Hamed Rezazadegan Tavakoli of Espoo (FI)
Amirhossein Hassankhani of Tampere (FI)
Collaborative Online Model Adaptation For Resource Constraint Devices - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240303486 titled 'Collaborative Online Model Adaptation For Resource Constraint Devices
The abstract of the patent application describes an apparatus that can process input using an efficient neural network, determine performance criteria for the network, and activate online learning based on these criteria. The apparatus can also receive video frames or features from the neural network, determine inference results based on these inputs, and transmit the results back to the network.
- Efficient neural network processing
- Performance criteria determination
- Online learning activation based on criteria
- Inference result determination from video frames or features
- Transmission of results back to the neural network
Potential Applications: - Video processing and analysis - Real-time decision-making systems - Autonomous vehicles - Surveillance systems - Medical imaging analysis
Problems Solved: - Enhancing the efficiency of neural network processing - Improving real-time decision-making capabilities - Streamlining data transmission and analysis processes
Benefits: - Faster and more accurate data processing - Enhanced performance of neural networks - Improved decision-making in various applications
Commercial Applications: Title: "Efficient Neural Network Apparatus for Real-time Video Analysis" This technology can be used in industries such as: - Security and surveillance - Healthcare - Automotive - Manufacturing - Entertainment
Questions about the technology: 1. How does the apparatus determine the performance criteria for the efficient neural network? - The apparatus uses algorithms to analyze the network's performance and identify areas for improvement. 2. What are the potential drawbacks of activating online learning based on performance criteria? - One potential drawback could be overfitting the network to specific criteria, limiting its overall adaptability and performance.
Original Abstract Submitted
an apparatus may be configured to: process at least one input with an efficient neural network; determine at least one performance criteria for the efficient neural network; and activate online learning for the efficient neural network based, at least partially, on the at least one performance criteria. an apparatus may be configured to: receive, from an efficient neural network, at least one video frame or at least one feature; determine at least one inference result based, at least partially, on the at least one video frame or the at least one feature; and transmit, to the efficient neural network, the at least one inference result.
