Waymo llc (20240135195). EFFICIENT SEARCH FOR DATA AUGMENTATION POLICIES simplified abstract

EFFICIENT SEARCH FOR DATA AUGMENTATION POLICIES

Organization Name

waymo llc

Inventor(s)

Zhaoqi Leng of Milpitas CA (US)

Guowang Li of Cupertino CA (US)

Chenxi Liu of Santa Clara CA (US)

Pei Sun of Palo Alto CA (US)

Tong He of Sunnyvale 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 20240135195 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 using data augmentation policies.

• Obtaining a training data set with multiple 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.

Key Features and Innovation

• Utilizes data augmentation policies to train machine learning models effectively.
• Generates a compact search space with global hyperparameters for efficient training.
• Enhances the performance of machine learning models by optimizing data augmentation policies.

Potential Applications

This technology can be applied in various fields such as image recognition, natural language processing, and predictive analytics.

Problems Solved

• Improves the accuracy and generalization of machine learning models.
• Streamlines the process of selecting data augmentation policies for training.

Benefits

• Enhances the performance of machine learning models.
• Increases the efficiency of training processes.
• Improves the overall quality of predictions and classifications.

Commercial Applications

Optimizing Data Augmentation Policies for Machine Learning Models This technology can be utilized by companies developing machine learning solutions for various industries such as healthcare, finance, and e-commerce.

Prior Art

There are existing methods for data augmentation in machine learning, but this patent application introduces a novel approach by generating a compact search space with global hyperparameters.

Frequently Updated Research

Research on the optimization of data augmentation policies for machine learning models is ongoing, with new techniques and algorithms being developed regularly.

Questions about Data Augmentation Policies for Machine Learning Models

Question 1

How does this technology improve the training process of machine learning models? Answer: This technology enhances the training process by selecting and applying optimized data augmentation policies, leading to improved model performance and generalization.

Question 2

What are the potential applications of this technology beyond machine learning? Answer: This technology can also be applied in fields such as computer vision, speech recognition, and anomaly detection to enhance the performance of various AI systems.

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.