18387901. LEARNING DEVICE, LEARNING METHOD, AND STORAGE MEDIUM simplified abstract (NEC Corporation)
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 18387901 titled 'LEARNING DEVICE, LEARNING METHOD, AND STORAGE MEDIUM
Simplified Explanation
The learning device for a neural network described in the abstract updates parameter values of both the partial network and normalization layers associated with different data groups and conditions of adversarial example generation.
- The device updates parameter values of the partial network and normalization layers based on the base data group and adversarial examples.
- It includes normalization layers associated with different data groups and conditions of adversarial example generation.
- The neural network consists of a partial network and normalization layers for different groups and conditions.
Potential Applications
This technology can be applied in:
- Image recognition systems
- Natural language processing models
- Fraud detection algorithms
Problems Solved
This innovation addresses issues such as:
- Improving the robustness of neural networks
- Enhancing the accuracy of machine learning models
- Mitigating the impact of adversarial attacks
Benefits
The benefits of this technology include:
- Increased security in AI systems
- Enhanced performance in classification tasks
- Improved generalization capabilities of neural networks
Potential Commercial Applications
The potential commercial applications of this technology include:
- Cybersecurity software
- Autonomous vehicles
- Healthcare diagnostics
Possible Prior Art
One possible prior art for this technology could be the use of adversarial training techniques in 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 other methods for adversarial training in neural networks.
What are the computational requirements for implementing this learning device in practical applications?
The article does not address the computational resources needed to deploy this learning device in real-world scenarios.
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.
