US 11,537,901 B2 · System and method for unsupervised domain adaptation with mixup training

Abstract

A system and method for domain adaptation involves a first domain and a second domain. A machine learning system is trained with first sensor data and first label data of the first domain. Second sensor data of a second domain is obtained. Second label data is generated via the machine learning system based on the second sensor data. Inter-domain sensor data is generated by interpolating the first sensor data of the first domain with respect to the second sensor data of the second domain. Inter-domain label data is generated by interpolating first label data of the first domain with respect to second label data of the second domain. The machine learning system is operable to generate inter-domain output data in response to the inter-domain sensor data. Inter-domain loss data is generated based on the inter-domain output data with respect to the inter-domain label data. Parameters of the machine learning system are updated upon optimizing final loss data that includes at least the inter-domain loss data. After domain adaptation, the machine learning system, which is operable in the first domain, is adapted to generate current label data that identifies current sensor data of the second domain.

Claims20

Dependent claims

A computer implemented method for unsupervised domain adaptation involving a first domain and a second domain, the method comprising:

obtaining a machine learning system that is trained with first sensor data and first label data of the first domain;
obtaining second sensor data of the second domain;
generating second label data, via the machine learning system, based on the second sensor data;
generating inter-domain sensor data by interpolating first sensor data of the first domain with respect to second sensor data of the second domain;
generating inter-domain label data by interpolating first label data of the first domain with respect to second label data of the second domain;
generating, via the machine learning system, inter-domain output databased on the inter-domain sensor data;
generating inter-domain loss data based on the inter-domain output data with respect to the inter-domain label data;
updating parameters of the machine learning system upon optimizing final loss data, the final loss data including the inter-domain loss data;
and
generating, via the machine learning system, current label data that classifies current sensor data of the second domain.

7 more elements

A system for domain adaptation, the system comprising:

a memory system including at least one non-transitory computer readable medium, the memory system storing a domain adaptation application and a machine learning system;
a processing system operatively connected to the memory system, the processing system including at least one processor that is configured to execute the domain adaptation application to implement a method that includes:
generating inter-domain sensor data by interpolating first sensor data of a first domain with respect to second sensor data of a second domain;
generating inter-domain label data by interpolating first label data of the first domain with respect to second label data of the second domain;
generating, via the machine learning system, inter-domain output data based on the inter-domain sensor data;
generating inter-domain loss data based on the inter-domain output data with respect to the inter-domain label data;
updating parameters of the machine learning system upon minimizing final loss data, the final loss data including at least the inter-domain loss data;
and
generating, via the machine learning system, current label data that classifies current sensor data of the second domain.

6 more elements

A non-transitory computer readable medium comprising computer readable data of a domain adaptation application that, when executed by a processing system with at least one processor, is configured to cause the processing system to implement a method that comprises:

generating inter-domain sensor data by interpolating first sensor data of a first domain with respect to second sensor data of a second domain;
generating inter-domain label data by interpolating first label data of the first domain with respect to second label data of the second domain;
generating, via a machine learning system, inter-domain output data based on the inter-domain sensor data;
generating inter-domain loss data based on the inter-domain output data with respect to the inter-domain label data;
updating parameters of the machine learning system upon minimizing final loss data, the final loss data including at least the inter-domain loss data;
and
generating, via the machine learning system, current label data that classifies current sensor data of the second domain.

4 more elements

Dependencies are inferred from the claim text.

System and method for unsupervised domain adaptation with mixup training

Life of this patent

System and method for unsupervised domain adaptation with mixup training

Life of this patent

Abstract

Claims20