18205035. TRAINING DATA GENERATION APPARATUS simplified abstract (NEC Corporation)
Contents
- 1 TRAINING DATA GENERATION APPARATUS
TRAINING DATA GENERATION APPARATUS
Organization Name
Inventor(s)
TRAINING DATA GENERATION APPARATUS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18205035 titled 'TRAINING DATA GENERATION APPARATUS
Simplified Explanation
The training data generation apparatus described in the patent application uses spatial data of an actual object to train a generative model for converting spatial data to a feature vector and vice versa. It also generates samples of the feature vector based on a probability distribution defined by a set of parameters, and uses these samples to generate training data for object recognition models.
- The apparatus uses spatial data of an object to train a generative model.
- It converts spatial data to a feature vector and vice versa.
- Samples of the feature vector are generated based on a probability distribution.
- These samples are used to create training data for object recognition models.
Potential Applications
The technology described in the patent application could have the following potential applications:
- Object recognition systems
- Image processing applications
- Machine learning algorithms
Problems Solved
The technology addresses the following problems:
- Efficient conversion between spatial data and feature vectors
- Generation of training data for object recognition models
- Improving the accuracy of object recognition systems
Benefits
The technology offers the following benefits:
- Improved object recognition accuracy
- Automated generation of training data
- Enhanced image processing capabilities
Potential Commercial Applications
The technology could be applied in various commercial settings, such as:
- Security systems for object recognition
- Medical imaging for diagnostic purposes
- Autonomous vehicles for object detection
Unanswered Questions
What are the limitations of the generative model in handling complex spatial data?
How does the training data generation apparatus handle noise or inaccuracies in the spatial data of objects?
Original Abstract Submitted
A training data generation apparatus uses spatial data of an actual object and thereby trains a generative model to perform conversion from the spatial data to a feature vector and conversion from the feature vector to spatial data. Moreover, the training data generation apparatus generates a sample of the feature vector as a realization value of a probability distribution defined by a set of parameters. Moreover, the training data generation apparatus generates training data used in training for an object recognition model based on the spatial data output from the generative model when the generated sample of the feature vector is input into the generative model.
