18584625. LEARNING DATA AUGMENTATION POLICIES simplified abstract (GOOGLE LLC)

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 18584625 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 model's performance.

Key Features and Innovation

• Training a machine learning model using multiple data augmentation policies over time.
• Determining the quality of each policy based on the model's performance.
• Selecting the best data augmentation policy for training the model effectively.

Potential Applications

This technology can be applied in various fields such as image recognition, natural language processing, and predictive analytics to enhance the accuracy and efficiency of machine learning models.

Problems Solved

This technology addresses the challenge of optimizing data augmentation techniques to improve the performance of machine learning models.

Benefits

• Enhanced accuracy and efficiency of machine learning models.
• Automated selection of data augmentation policies based on performance metrics.
• Improved training process for machine learning tasks.

Commercial Applications

• "Optimizing Data Augmentation Policies for Machine Learning Models" can be used in industries such as healthcare, finance, and e-commerce to improve decision-making processes and predictive modeling.

Prior Art

Readers interested in prior art related to this technology can explore research papers, patents, and academic publications on data augmentation techniques for machine learning models.

Frequently Updated Research

Researchers are continually exploring new methods and algorithms for optimizing data augmentation policies in machine learning to further enhance model performance.

Questions about Optimizing Data Augmentation Policies for Machine Learning Models

What are the key benefits of using data augmentation policies in training machine learning models?

Data augmentation policies help improve the generalization and robustness of machine learning models by increasing the diversity and quantity of training data.

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

The selection of data augmentation policies directly influences the model's ability to learn and generalize patterns from the training data, ultimately affecting its performance in real-world applications.

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.