Zero-shot learning using multi-scale manifold alignment
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.
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