Nec corporation (20240160946). LEARNING DEVICE, LEARNING METHOD, AND STORAGE MEDIUM simplified abstract
Contents
- 1 LEARNING DEVICE, LEARNING METHOD, AND STORAGE MEDIUM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 LEARNING DEVICE, LEARNING METHOD, AND STORAGE MEDIUM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
LEARNING DEVICE, LEARNING METHOD, AND STORAGE MEDIUM
Organization Name
Inventor(s)
LEARNING DEVICE, LEARNING METHOD, AND STORAGE MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240160946 titled 'LEARNING DEVICE, LEARNING METHOD, AND STORAGE MEDIUM
Simplified Explanation
The patent application describes a learning device for a neural network that updates parameter values of both the partial network and normalization layers associated with different data groups and conditions of adversarial example generation.
- The learning device updates parameter values of the partial network and normalization layers based on the base data group and adversarial examples.
- The neural network includes a partial network, normalization layers for the base data group, and normalization layers for each condition of adversarial example generation.
Potential Applications
This technology could be applied in various fields such as image recognition, natural language processing, and anomaly detection.
Problems Solved
1. Improved robustness of neural networks against adversarial attacks. 2. Enhanced performance and accuracy of neural networks in different conditions and data groups.
Benefits
1. Increased security and reliability of neural network systems. 2. Better generalization and adaptability to different scenarios and data distributions.
Potential Commercial Applications
The technology could be utilized in cybersecurity systems, autonomous vehicles, healthcare diagnostics, and financial fraud detection.
Possible Prior Art
One possible prior art could be research on adversarial training techniques for neural networks to improve robustness against adversarial attacks.
Unanswered Questions
How does this technology compare to existing methods for adversarial training in neural networks?
This article does not provide a direct comparison with existing methods for adversarial training in neural networks.
What are the specific performance improvements achieved by this learning device in neural networks?
The article does not detail the specific performance improvements achieved by this learning device in neural networks.
Original Abstract Submitted
a learning device for a neural network uses the base data group to update a parameter value of the partial network and a parameter value of the normalization layer associated with the entire base data group, and uses each group of adversarial examples of each adversarial example generation condition to update the parameter value of the partial network and the parameter value of the normalization layer associated with the condition. the neural network includes a partial network, a normalization layer associated with the entirety of a base data group including a plurality of data, and a normalization layer associated with each condition of adversarial example generation.
