18469166. Method for Generating Additional Training Data for Training a Machine Learning Algorithm simplified abstract (Robert Bosch GmbH)
Contents
- 1 Method for Generating Additional Training Data for Training a Machine Learning Algorithm
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Method for Generating Additional Training Data for Training a Machine Learning Algorithm - 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
Method for Generating Additional Training Data for Training a Machine Learning Algorithm
Organization Name
Inventor(s)
Amulya Hiremath of Bangalore (IN)
Barbara Rakitsch of Stuttgart (DE)
Gonca Guersun of Stuttgart (DE)
Joerg Wagner of Renningen (DE)
Michael Herman of Sindelfingen (DE)
Method for Generating Additional Training Data for Training a Machine Learning Algorithm - A simplified explanation of the abstract
This abstract first appeared for US patent application 18469166 titled 'Method for Generating Additional Training Data for Training a Machine Learning Algorithm
Simplified Explanation
The abstract describes a method for generating additional training data for training a machine learning algorithm by transforming the training data into a graph structure and modifying it to create new training data.
- The method involves providing labeled sensor data from at least one sensor.
- The training data is transformed into a graph structure where nodes represent objects in the sensor data.
- The starting node in the graph represents the sensor's position relative to the objects.
- Additional training data is generated by modifying the graph structure.
Potential Applications
This technology could be applied in various fields such as autonomous driving, robotics, and computer vision for improving the accuracy and performance of machine learning models.
Problems Solved
This method helps address the challenge of limited training data by generating additional data through graph structure transformations, leading to better-trained machine learning algorithms.
Benefits
The benefits of this technology include enhanced model accuracy, improved generalization, and increased robustness of machine learning algorithms.
Potential Commercial Applications
Potential commercial applications of this technology include developing advanced driver assistance systems, optimizing industrial automation processes, and enhancing surveillance systems for security purposes.
Possible Prior Art
One possible prior art could be the use of data augmentation techniques in machine learning to generate additional training data for improving model performance. Another could be the application of graph-based data representations in various machine learning tasks.
What are the specific sensor data types used in this method?
The abstract does not specify the types of sensor data used in the method. It would be helpful to know if the method is applicable to a wide range of sensor data types or if it is limited to specific types.
How does modifying the graph structure lead to the generation of additional training data?
The abstract mentions modifying the graph structure to generate additional training data, but it does not provide details on the specific modifications made or how they contribute to creating new training data. Understanding this process in more detail would provide insights into the effectiveness of the method.
Original Abstract Submitted
A method for generating additional training data for training a machine learning algorithm is disclosed. The method includes (i) providing training data for training the machine learning algorithm, wherein the training data includes labeled sensor data from at least one sensor, (ii) transforming the training data for training the machine learning algorithm in a graph structure, wherein nodes in the graph structure represent objects represented in the corresponding sensor data, and wherein a starting node of the graph structure represents the position of the at least one sensor with respect to the objects represented in the corresponding sensor data, and (iii) generating additional training data for training the machine learning model by modifying the graph structure.
