Nvidia corporation (20240104698). NEURAL NETWORK-BASED PERTURBATION REMOVAL simplified abstract
Contents
- 1 NEURAL NETWORK-BASED PERTURBATION REMOVAL
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 NEURAL NETWORK-BASED PERTURBATION REMOVAL - 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
NEURAL NETWORK-BASED PERTURBATION REMOVAL
Organization Name
Inventor(s)
Weili Nie of Sunnyvale CA (US)
Yujia Huang of Pasadena CA (US)
Chaowei Xiao of Seattle WA (US)
Arash Vahdat of Mountain View CA (US)
Anima Anandkumar of Pasadena CA (US)
NEURAL NETWORK-BASED PERTURBATION REMOVAL - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240104698 titled 'NEURAL NETWORK-BASED PERTURBATION REMOVAL
Simplified Explanation
The patent application describes a method for removing unintended variations introduced into data by generating a first image of an object based on adding noise to and removing noise from a second image of the object.
- Explanation:
* The innovation involves generating a new image of an object by manipulating the noise present in a previous image. * By adding and removing noise, the system can enhance the quality and accuracy of the image data. * This process helps in reducing unintended variations and improving the overall data integrity.
Potential Applications
The technology can be applied in:
- Medical imaging for enhancing diagnostic accuracy.
- Surveillance systems for improving image clarity.
- Quality control in manufacturing processes.
Problems Solved
The technology addresses issues such as:
- Data corruption due to noise interference.
- Inaccurate analysis caused by unintended variations.
- Poor image quality affecting decision-making processes.
Benefits
The benefits of this technology include:
- Enhanced data accuracy and integrity.
- Improved image quality for better analysis.
- Increased reliability in data-driven decision-making.
Potential Commercial Applications
A potential commercial application of this technology could be in:
- Image processing software for various industries.
- Data analysis tools for research institutions.
- Quality assurance systems for manufacturing companies.
Possible Prior Art
One possible prior art for this technology could be:
- Image denoising algorithms used in digital photography.
- Signal processing techniques for noise reduction in audio files.
Unanswered Questions
How does this technology compare to existing noise removal methods in terms of efficiency and accuracy?
The efficiency and accuracy of this technology compared to existing noise removal methods are not explicitly discussed in the abstract. It would be beneficial to understand how this innovation stands out in terms of performance metrics.
What are the potential limitations or constraints of implementing this technology in real-world applications?
The abstract does not mention any potential limitations or constraints of implementing this technology. It would be important to explore factors such as computational resources, processing time, and scalability when applying this innovation in practical scenarios.
Original Abstract Submitted
apparatuses, systems, and techniques are presented to remove unintended variations introduced into data. in at least one embodiment, a first image of an object can be generated based, at least in part, upon adding noise to, and removing the noise from, a second image of the object.
