Domain adaption learning system
AssigneeHRL Laboratories, LLC
InventorsZachary Murez, Soheil Kolouri, Kyungnam Kim
Life of this patent
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Abstract
Described is a system for adapting a deep convolutional neural network (CNN). A deep CNN is first trained on an annotated source image domain. The deep CNN is adapted to a new target image domain without requiring new annotations by determining domain agnostic features that map from the annotated source image domain and a target image domain to a joint latent space, and using the domain agnostic features to map the joint latent space to annotations for the target image domain.
Claims23
A system for adapting a deep convolutional neural network (CNN), the system comprising:
- one or more processors and a non-transitory computer-readable medium having executable instructions encoded thereon such that when executed, the one or more processors perform operations of:
- training a deep CNN on an annotated source image domain;
- and
- adapting the deep CNN to a new target image domain without requiring new annotations by determining domain agnostic features that map from the annotated source image domain and a target image domain to a joint latent space and using the domain agnostic features to map the joint latent space to annotations for the target image domain.
A computer implemented method for adapting a deep convolutional neural network (CNN), the method comprising an act of:
- causing one or more processors to execute instructions encoded on a non-transitory computer-readable medium, such that upon execution, the one or more processors perform operations of:
- training a deep CNN on an annotated source image domain;
- and
- adapting the deep CNN to a new target image domain without requiring new annotations by determining domain agnostic features that map from the annotated source image domain and a target image domain to a joint latent space and using the domain agnostic features to map the joint latent space to annotations for the target image domain.
A computer program product for adapting a deep convolutional neural network (CNN), the computer program product comprising:
- computer-readable instructions stored on a non-transitory computer-readable medium that are executable by a computer having one or more processors for causing the processor to perform operations of:
- training a deep CNN on an annotated source image domain;
- and
- adapting the deep CNN to a new target image domain without requiring new annotations by determining domain agnostic features that map from the annotated source image domain and a target image domain to a joint latent space and using the domain agnostic features to map the joint latent space to annotations for the target image domain.
Dependencies are inferred from the claim text.