Waymo llc (20240232647). EFFICIENT SEARCH FOR DATA AUGMENTATION POLICIES simplified abstract
Contents
EFFICIENT SEARCH FOR DATA AUGMENTATION POLICIES
Organization Name
Inventor(s)
Zhaoqi Leng of Milpitas CA (US)
Guowang Li of Cupertino CA (US)
Chenxi Liu of Santa Clara CA (US)
Dragomir Anguelov of San Francisco CA (US)
Mingxing Tan of Newark CA (US)
EFFICIENT SEARCH FOR DATA AUGMENTATION POLICIES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240232647 titled 'EFFICIENT SEARCH FOR DATA AUGMENTATION POLICIES
Simplified Explanation: The patent application describes methods, systems, and apparatus for training a machine learning model on training data by generating data augmentation policies from a compact search space.
Key Features and Innovation:
- Obtaining a training data set with training inputs
- Defining an original search space of candidate data augmentation policies
- Generating a compact search space with global hyperparameters
- Training the machine learning model using final data augmentation policies from the compact search space
Potential Applications: This technology can be applied in various fields such as image recognition, natural language processing, and predictive analytics.
Problems Solved: This technology addresses the need for efficient data augmentation techniques to improve the performance of machine learning models.
Benefits:
- Enhanced accuracy and generalization of machine learning models
- Reduction in overfitting and improved robustness
- Streamlined training process with optimized data augmentation policies
Commercial Applications: The technology can be utilized in industries such as healthcare for medical image analysis, finance for fraud detection, and e-commerce for personalized recommendations.
Prior Art: Researchers can explore prior art related to data augmentation techniques in machine learning and optimization algorithms for model training.
Frequently Updated Research: Stay updated on advancements in data augmentation methods, hyperparameter optimization, and machine learning model training techniques.
Questions about Data Augmentation Policies: 1. How do data augmentation policies impact the performance of machine learning models? 2. What are the challenges in selecting the most effective data augmentation policies for training a model?
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model on training data. in one aspect, one of the methods include: obtaining a training data set comprising a plurality of training inputs; obtaining data defining an original search space of a plurality of candidate data augmentation policies; generating, from the original search space, a compact search space that has one or more global hyperparameters; and training the machine learning model on the training data using one or more final data augmentation policies generated from the compact search space.
