Tesla, inc. (20240177455). SYSTEMS AND METHODS FOR TRAINING MACHINE MODELS WITH AUGMENTED DATA simplified abstract
Contents
- 1 SYSTEMS AND METHODS FOR TRAINING MACHINE MODELS WITH AUGMENTED DATA
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS AND METHODS FOR TRAINING MACHINE MODELS WITH AUGMENTED DATA - 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
SYSTEMS AND METHODS FOR TRAINING MACHINE MODELS WITH AUGMENTED DATA
Organization Name
Inventor(s)
Matthew John Cooper of Providence RI (US)
Paras Jagdish Jain of Cupertino CA (US)
Harsimran Singh Sidhu of Fremont CA (US)
SYSTEMS AND METHODS FOR TRAINING MACHINE MODELS WITH AUGMENTED DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240177455 titled 'SYSTEMS AND METHODS FOR TRAINING MACHINE MODELS WITH AUGMENTED DATA
Simplified Explanation
The abstract describes systems and methods for training machine models with augmented data. This involves identifying a set of images captured by cameras, generating augmented images for some of these images, associating the augmented images with training outputs, and training predictive computer models based on the images and augmented images.
- Identifying a set of images captured by cameras affixed to image collection systems
- Generating augmented images for some of the images using image manipulation functions
- Associating augmented images with training outputs to train predictive computer models
- Maintaining camera properties of the original images in the augmented images
Potential Applications
This technology can be applied in various fields such as computer vision, image recognition, and machine learning.
Problems Solved
This technology helps improve the accuracy and performance of machine learning models by training them with augmented data, which can enhance their ability to predict outcomes based on images.
Benefits
The benefits of this technology include increased model accuracy, improved generalization to new data, and enhanced performance in tasks such as object recognition and classification.
Potential Commercial Applications
Commercial applications of this technology include developing advanced image recognition systems, autonomous vehicles, surveillance systems, and other applications that rely on accurate image analysis.
Possible Prior Art
Prior art in this field may include research on data augmentation techniques for machine learning models, image manipulation functions, and computer vision algorithms.
What are the limitations of this technology in real-world applications?
This technology may face challenges in handling complex and dynamic environments where the training data may not fully represent all possible scenarios.
How does this technology compare to traditional machine learning methods without augmented data?
This technology offers the advantage of improving model performance and generalization by training on augmented data, which can lead to better results compared to traditional methods that rely solely on original data.
Original Abstract Submitted
systems and methods for training machine models with augmented data. an example method includes identifying a set of images captured by a set of cameras while affixed to one or more image collection systems. for each image in the set of images, a training output for the image is identified. for one or more images in the set of images, an augmented image for a set of augmented images is generated. generating an augmented image includes modifying the image with an image manipulation function that maintains camera properties of the image. the augmented training image is associated with the training output of the image. a set of parameters of the predictive computer model are trained to predict the training output based on an image training set including the images and the set of augmented images.
