MACHINE LEARNING A FEATURE DETECTOR USING SYNTHETIC TRAINING DATA
First Claim
1. A method comprising:
- defining, via a network apparatus, first probe data, an instance of first probe data comprising one or more labels and sensor data of a first probe style;
generating, via a style transfer model operating at least in part on the network apparatus, training data based on at least a portion of the first probe data, wherein an instance of training data corresponds to an instance of first probe data and sensor data in a second probe style, the second probe style being different from the first probe style;
training, via the network apparatus, a second probe style model using machine learning and at least a portion of the training data, wherein (a) the second probe style model is used to analyze one or more instances of second probe data of the second probe style to extract map information from the second probe data and (b) each instance of second probe data is captured by one or more second probe sensors of a second probe apparatus.
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Abstract
Synthetic training information/data of a second probe style is generated based on first probe information/data of a first probe style using a style transfer model. First probe information/data is defined. An instance of first probe information/data comprises labels and first probe style sensor information/data. A style transfer model generates training information/data based on at least a portion of the first probe information/data. An instance of training information/data corresponds to an instance of first probe information/data and comprises second probe style sensor information/data. The first and second probe styles are different. A second probe style model is trained using machine learning and the training information/data. The second probe style model is used to analyze second probe style second probe information/data to extract map information/data from the second probe information/data. Each instance of second probe data is captured by one or more second probe sensors of a second probe apparatus.
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Citations
20 Claims
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1. A method comprising:
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defining, via a network apparatus, first probe data, an instance of first probe data comprising one or more labels and sensor data of a first probe style; generating, via a style transfer model operating at least in part on the network apparatus, training data based on at least a portion of the first probe data, wherein an instance of training data corresponds to an instance of first probe data and sensor data in a second probe style, the second probe style being different from the first probe style; training, via the network apparatus, a second probe style model using machine learning and at least a portion of the training data, wherein (a) the second probe style model is used to analyze one or more instances of second probe data of the second probe style to extract map information from the second probe data and (b) each instance of second probe data is captured by one or more second probe sensors of a second probe apparatus. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. An apparatus comprising at least one processor, a communications interface configured for communicating via at least one network, and at least one memory storing computer program code, the at least one memory and the computer program code configured to, with the processor, cause the apparatus to at least:
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define first probe data, an instance of first probe data comprising one or more labels and sensor data of a first probe style; generate, via a style transfer model operating at least in part on the network apparatus, training data based on at least a portion of the first probe data, wherein an instance of training data corresponds to an instance of first probe data and sensor data in a second probe style, the second probe style being different from the first probe style; train a second probe style model using machine learning and at least a portion of the training data, wherein (a) the second probe style model is used to analyze one or more instances of second probe data of the second probe style to extract map information from the second probe data and (b) each instance of second probe data is captured by one or more second probe sensors of a second probe apparatus. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18)
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19. A computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising executable portions configured, when executed by a processor of a network apparatus, to cause the network apparatus to:
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define first probe data, an instance of first probe data comprising one or more labels and sensor data of a first probe style; generate, via a style transfer model operating at least in part on the network apparatus, training data based on at least a portion of the first probe data, wherein an instance of training data corresponds to an instance of first probe data and sensor data in a second probe style, the second probe style being different from the first probe style; train a second probe style model using machine learning and at least a portion of the training data, wherein (a) the second probe style model is used to analyze one or more instances of second probe data of the second probe style to extract map information from the second probe data and (b) each instance of second probe data is captured by one or more second probe sensors of a second probe apparatus. - View Dependent Claims (20)
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Specification