Invention Grant
- Patent Title: Zero-shot learning using multi-scale manifold alignment
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Application No.: US15847895Application Date: 2017-12-19
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Publication No.: US10592788B2Publication Date: 2020-03-17
- Inventor: Shay Deutsch , Kyungnam Kim , Yuri Owechko
- Applicant: HRL Laboratories, LLC
- Applicant Address: US CA Malibu
- Assignee: HRL Laboratories, LLC
- Current Assignee: HRL Laboratories, LLC
- Current Assignee Address: US CA Malibu
- Agency: Tope-McKay & Associates
- Main IPC: G06K9/62
- IPC: G06K9/62 ; G06K9/72

Abstract:
Described is a system for recognition of unseen and untrained patterns. A graph is generated based on visual features from input data, the input data including labeled instances and unseen instances. Semantic representations of the input data are assigned as graph signals based on the visual features. The semantic representations are aligned with visual representations of the input data using a regularization method applied directly in a spectral graph wavelets (SGW) domain. The semantic representations are then used to generate labels for the unseen instances. The unseen instances may represent unknown conditions for an autonomous vehicle.
Public/Granted literature
- US20180197050A1 ZERO-SHOT LEARNING USING MULTI-SCALE MANIFOLD ALIGNMENT Public/Granted day:2018-07-12
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