Method for model adaptation, electronic device and computer program product

An electronic device, comprising:

• at least one processor;
• and
• at least one memory storing computer program instructions, the at least one memory and the computer program instructions being configured, with the at least one processor, to cause the electronic device to perform acts comprising:
• processing first input data by using a first machine learning model having first parameter set values, to obtain first feature information of the first input data, the first machine learning model having a capability of self-ordering and the first parameter set values being updated after the processing of the first input data;
• generating a first classification result for the first input data based on the first feature information by using a second machine learning model having second parameter set values;
• processing second input data by using the first machine learning model having the updated first parameter set values, to obtain second feature information of the second input data;
• generating a second classification result for the second input data based on the second feature information by using the second machine learning model having the second parameter set values;
• and
• generating adaptive change output data based on changes in the first input data and the second input data;
• wherein the acts further comprise:
• storing at least one of the first feature information and the second feature information;
• and
• in accordance with a presence of at least one of a first ground-truth classification result for the first input data and a second ground-truth classification result for the second input data, and in accordance with a determination that an update of the second machine learning model is triggered, re-training a duplicated model of the second machine learning model by using at least one of a pair of the first feature information and the first ground-truth classification result and a pair of the second feature information and the second ground-truth classification result, so as to update the second parameter set values;
• wherein the first machine learning model comprises an unsupervised spiking neural network;
• and
• wherein the second machine learning model comprises a machine learning model other than an unsupervised spiking neural network and a supervised spiking neural network.

A computer program product which is tangibly stored on a non-transitory computer readable medium and comprises machine-executable instructions which, when executed by a device, causing the device to:

• process first input data by using a first machine learning model having first parameter set values, to obtain first feature information of the first input data, the first machine learning model having a capability of self-ordering and the first parameter set values being updated after the processing of the first input data;
• generate a first classification result for the first input data based on the first feature information by using a second machine learning model having second parameter set values;
• process second input data by using the first machine learning model having the updated first parameter set values, to obtain second feature information of the second input data;
• generate a second classification result for the second input data based on the second feature information by using the second machine learning model having the second parameter set values;
• and
• generate adaptive change output data based on changes in the first input data and the second input data;
• wherein the machine-executable instructions, when executed by the device, further cause the device to:
• store at least one of the first feature information and the second feature information;
• and
• in accordance with a presence of at least one of a first ground-truth classification result for the first input data and a second ground-truth classification result for the second input data, and in accordance with a determination that an update of the second machine learning model is triggered, re-train a duplicated model of the second machine learning model by using at least one of a pair of the first feature information and the first ground-truth classification result;
• wherein the first machine learning model comprises an unsupervised spiking neural network;
• and
• wherein the second machine learning model comprises a machine learning model other than an unsupervised spiking neural network and a supervised spiking neural network.