Invention Grant
- Patent Title: Training neural networks using data augmentation policies
-
Application No.: US16833449Application Date: 2020-03-27
-
Publication No.: US11205099B2Publication Date: 2021-12-21
- Inventor: Jonathon Shlens , Quoc V. Le , Ekin Dogus Cubuk , Barret Zoph
- Applicant: Google LLC
- Applicant Address: US CA Mountain View
- Assignee: Google LLC
- Current Assignee: Google LLC
- Current Assignee Address: US CA Mountain View
- Agency: Fish & Richardson P.C.
- Main IPC: G06K9/62
- IPC: G06K9/62 ; G06N3/08 ; G06N3/04

Abstract:
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model. One of the methods includes obtaining a training data set for training a machine learning model, the training data set comprising a plurality of training inputs; determining a plurality of data augmentation policies, wherein each data augmentation policy defines a procedure for processing a training input to generate a transformed training input; for each data augmentation policy, training the machine learning model using the data augmentation policy; determining, for each data augmentation policy, a quality measure of the machine learning model that has been trained using the data augmentation policy; and selecting a final data augmentation policy based using the quality measures of the machine learning models.
Public/Granted literature
- US20210097348A1 TRAINING NEURAL NETWORKS USING DATA AUGMENTATION POLICIES Public/Granted day:2021-04-01
Information query