Invention Grant
- Patent Title: Image transformation with a hybrid autoencoder and generative adversarial network machine learning architecture
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Application No.: US16206538Application Date: 2018-11-30
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Publication No.: US10803347B2Publication Date: 2020-10-13
- Inventor: Jason Salavon
- Applicant: The University of Chicago
- Applicant Address: US IL Chicago
- Assignee: The University of Chicago
- Current Assignee: The University of Chicago
- Current Assignee Address: US IL Chicago
- Agency: McDonnell Boehnen Hulbert & Berghoff LLP
- Main IPC: G06K9/62
- IPC: G06K9/62 ; G06F16/55 ; G06N3/08 ; G06N3/04 ; G06F16/583

Abstract:
An encoder artificial neural network (ANN) may be configured to receive an input image patch and produce a feature vector therefrom. The encoder ANN may have been trained with a first plurality of domain training images such that an output image patch visually resembling the input image patch can be generated from the feature vector. A generator ANN may be configured to receive the feature vector and produce a generated image patch from the first feature vector. The generator ANN may have been trained with feature vectors derived from a first plurality of domain training images and a second plurality of generative training images such that the generated image patch visually resembles the input image patch but is constructed of a newly-generated image elements visually resembling one or more image patches from the second plurality of generative training images.
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