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

=LEARNING DATA AUGMENTATION POLICIES=


==Organization Name==

[[:Category:GOOGLE LLC|GOOGLE LLC]]


[[Category:GOOGLE LLC]]

==Inventor(s)==

[[:Category:Vijay Vasudevan of Los Altos Hills CA (US)|Vijay Vasudevan of Los Altos Hills CA (US)]][[Category:Vijay Vasudevan of Los Altos Hills CA (US)]]

[[:Category:Barret Zoph of San Francisco CA (US)|Barret Zoph of San Francisco CA (US)]][[Category:Barret Zoph of San Francisco CA (US)]]

[[:Category:Ekin Dogus Cubuk of Sunnyvale CA (US)|Ekin Dogus Cubuk of Sunnyvale CA (US)]][[Category:Ekin Dogus Cubuk of Sunnyvale CA (US)]]

[[:Category:Quoc V. Le of Sunnyvale CA (US)|Quoc V. Le of Sunnyvale CA (US)]][[Category: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.



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