Context dependent transfer learning adaptation to achieve fast performance in inference and update

Autonomous vehicles may utilize neural networks for image classification in order to navigate infrastructures and foreign environments, using context dependent transfer learning adaptation. Techniques include receiving a transferable output layer from the infrastructure, which is a model suitable fo...

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Bibliographic Details
Main Authors Gusikhin, Oleg Yurievitch, Makke, Omar
Format Patent
LanguageEnglish
Published 04.04.2023
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE ORDIFFERENT FUNCTION
CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES
CONTROLLING
COUNTING
ELECTRIC COMMUNICATION TECHNIQUE
ELECTRICITY
PERFORMING OPERATIONS
PHYSICS
REGULATING
ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TOTHE CONTROL OF A PARTICULAR SUB-UNIT
SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
TRANSPORTING
VEHICLES IN GENERAL
WIRELESS COMMUNICATIONS NETWORKS
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Summary:Autonomous vehicles may utilize neural networks for image classification in order to navigate infrastructures and foreign environments, using context dependent transfer learning adaptation. Techniques include receiving a transferable output layer from the infrastructure, which is a model suitable for the infrastructure and the local environment. Sensor data from the autonomous vehicle may then be passed through the neural network and classified. The classified data can map to an output of the transferable output layer, allowing the autonomous vehicle to obtain particular outputs for particular context dependent inputs, without requiring further parameters within the neural network.
Bibliography:Application Number: US202016836766