Nvidia corporation (20240127075). SYNTHETIC DATASET GENERATOR simplified abstract
Contents
- 1 SYNTHETIC DATASET GENERATOR
SYNTHETIC DATASET GENERATOR
Organization Name
Inventor(s)
Shalini De Mello of San Francisco CA (US)
Christian Jacobsen of Ann Arbor MI (US)
Stephen Tyree of University City MO (US)
Alice Li of Santa Clara CA (US)
Wonmin Byeon of Santa Cruz CA (US)
Shangru Li of Philadelphia PA (US)
SYNTHETIC DATASET GENERATOR - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240127075 titled 'SYNTHETIC DATASET GENERATOR
Simplified Explanation
Machine learning is a process that learns a model from a given dataset, where the model can then be used to make a prediction about new data. In order to reduce the costs associated with collecting and labeling real-world datasets for use in training the model, computer processes can synthetically generate datasets which simulate real-world data. The present disclosure improves the effectiveness of such synthetic datasets for training machine learning models used in real-world applications, in particular by generating a synthetic dataset that is specifically targeted to a specified downstream task (e.g. a particular computer vision task, a particular natural language processing task, etc.).
- The innovation in this patent application involves improving the effectiveness of synthetic datasets for training machine learning models by generating datasets specifically targeted to a specified downstream task.
- By synthetically generating datasets that simulate real-world data, the costs associated with collecting and labeling real-world datasets for training machine learning models can be reduced.
Potential Applications
The technology described in this patent application could be applied in various fields such as:
- Computer vision
- Natural language processing
- Fraud detection
- Medical diagnosis
Problems Solved
The technology solves the following problems:
- High costs associated with collecting and labeling real-world datasets for training machine learning models
- Lack of targeted synthetic datasets for specific downstream tasks
Benefits
The benefits of this technology include:
- Cost reduction in dataset collection and labeling
- Improved effectiveness of synthetic datasets for training machine learning models
Potential Commercial Applications
A potential commercial application of this technology could be in:
- Developing customized machine learning models for specific industries or tasks
Possible Prior Art
One possible prior art related to this technology is the use of generative adversarial networks (GANs) to generate synthetic data for training machine learning models.
Unanswered Questions
How does this technology compare to other methods of generating synthetic datasets for machine learning models?
This article does not provide a comparison with other methods of generating synthetic datasets for machine learning models. It would be interesting to know the advantages and disadvantages of this technology compared to existing methods.
What are the limitations of using synthetic datasets in training machine learning models for real-world applications?
The article does not discuss the limitations of using synthetic datasets in training machine learning models for real-world applications. Understanding the potential drawbacks or challenges of this approach would provide a more comprehensive view of the technology.
Original Abstract Submitted
machine learning is a process that learns a model from a given dataset, where the model can then be used to make a prediction about new data. in order to reduce the costs associated with collecting and labeling real world datasets for use in training the model, computer processes can synthetically generate datasets which simulate real world data. the present disclosure improves the effectiveness of such synthetic datasets for training machine learning models used in real world applications, in particular by generating a synthetic dataset that is specifically targeted to a specified downstream task (e.g. a particular computer vision task, a particular natural language processing task, etc.).
