Google llc (20240242125). LEARNING DATA AUGMENTATION POLICIES simplified abstract

LEARNING DATA AUGMENTATION POLICIES

Organization Name

google llc

Inventor(s)

Vijay Vasudevan of Los Altos Hills CA (US)

Barret Zoph of San Francisco CA (US)

Ekin Dogus Cubuk of Sunnyvale CA (US)

Quoc V. Le of Sunnyvale CA (US)

LEARNING DATA AUGMENTATION POLICIES - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240242125 titled 'LEARNING DATA AUGMENTATION POLICIES

Simplified Explanation

The patent application describes methods, systems, and apparatus for learning a data augmentation policy to train a machine learning model. This involves generating and selecting data augmentation policies based on quality measures to improve the performance of the machine learning model.

Key Features and Innovation

• Learning a data augmentation policy for training a machine learning model
• Determining multiple data augmentation policies based on quality measures
• Training the machine learning model using the selected data augmentation policy
• Improving the performance of the machine learning model through optimized data augmentation policies

Potential Applications

The technology can be applied in various fields such as image recognition, natural language processing, and predictive analytics where machine learning models are used.

Problems Solved

This technology addresses the challenge of optimizing data augmentation policies to enhance the performance of machine learning models.

Benefits

• Improved accuracy and efficiency of machine learning models
• Enhanced performance in various machine learning tasks
• Automated selection of data augmentation policies for training models

Commercial Applications

• Image recognition software for improved accuracy
• Natural language processing tools for better language understanding
• Predictive analytics models for more accurate predictions

Prior Art

Readers can explore prior research on data augmentation policies in machine learning to understand the evolution of this technology.

Frequently Updated Research

Stay updated on the latest advancements in data augmentation policies for machine learning models to ensure the application of cutting-edge techniques.

Questions about Data Augmentation Policies

How do data augmentation policies impact the performance of machine learning models?

Data augmentation policies can significantly improve the accuracy and efficiency of machine learning models by providing additional training data.

What are the key considerations when selecting a data augmentation policy for training a machine learning model?

Key considerations include the quality measures used to evaluate the effectiveness of data augmentation policies, as well as the specific machine learning task being performed.

Original Abstract Submitted

methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for learning a data augmentation policy for training a machine learning model. in one aspect, a method includes: receiving training data for training a machine learning model to perform a particular machine learning task; determining multiple data augmentation policies, comprising, at each of multiple time steps: generating a current data augmentation policy based on quality measures of data augmentation policies generated at previous time steps; training a machine learning model on the training data using the current data augmentation policy; and determining a quality measure of the current data augmentation policy using the machine learning model after it has been trained using the current data augmentation policy; and selecting a final data augmentation policy based on the quality measures of the determined data augmentation policies.