Vector bypass for generative adversarial image segmentation
AssigneeInternational Business Machines Corporation
InventorsHaoxiang Qiu, Takayuki Osogami, Takayuki Katsuki, Tomoya Sakai, Tadanobu Inoue
Life of this patent
Estimating the term…
Abstract
A method, computer system, and a computer program product are provided. A visual inspection machine learning model is trained using a generative adversarial network. Within the generative adversarial network a vector bypass is implemented. By transmitting a vector embedding representation of an unlabeled image through the vector bypass, the vector embedding representation is transmitted around the visual inspection machine learning model and to a generator to assist with image reconstruction.
Claims20
A computer-implemented method comprising:
- training a visual inspection machine learning model using a generative adversarial network;
- and
- implementing within the generative adversarial network a vector bypass through which a vector embedding representation of an unlabeled image is transmitted around the visual inspection machine learning model and to a generator to assist with image reconstruction, wherein the vector embedding representation comprises a one-dimensional hidden embedding representing an image secondary feature of the unlabeled image.
A computer system comprising:
- one or more processors,
- one or more computer-readable memories, and
- program instructions stored on at least one of the one or more computer-readable memories for execution by at least one of the one or more processors to cause the computer system to:
- train a visual inspection machine learning model using a generative adversarial network;
- and
- implement within the generative adversarial network a vector bypass through which a vector embedding representation of an unlabeled image is transmitted around the visual inspection machine learning model and to a generator to assist with image reconstruction, wherein the vector embedding representation comprises a one-dimensional hidden embedding representing an image secondary feature of the unlabeled image.
A computer program product comprising:
- a non-transitory computer-readable storage medium, and
- program instructions embodied with the non-transitory computer-readable storage medium, the program instructions executable by a computer to cause the computer to:
- train a visual inspection machine learning model using a generative adversarial network;
- and
- implement within the generative adversarial network a vector bypass through which a vector embedding representation of an unlabeled image is transmitted around the visual inspection machine learning model and to a generator to assist with image reconstruction, wherein the vector embedding representation comprises a one-dimensional hidden embedding representing an image secondary feature of the unlabeled image.
Dependencies are inferred from the claim text.