US Patent Application 17745462. METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR EVALUATING SAMPLES simplified abstract
METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR EVALUATING SAMPLES
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METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR EVALUATING SAMPLES - A simplified explanation of the abstract
- This abstract for appeared for US patent application number 17745462 Titled 'METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR EVALUATING SAMPLES'
Simplified Explanation
This abstract describes a method, electronic device, and computer program for evaluating samples. The method involves receiving a classification model from a cloud server and acquiring a sample distribution for each class in the model. An input sample marked as a certain class is acquired and checked against the corresponding sample distribution. If the input sample conforms to the distribution, it is identified as a trusted sample, indicating that it has been correctly labeled. This method helps recognize noise samples with incorrect labels, preventing model degradation during updates.
Original Abstract Submitted
Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for evaluating samples. The method includes receiving, at an edge device, a classification model from a cloud server. The method further includes acquiring a sample distribution corresponding to each class in a plurality of classes of the classification model. The method further includes acquiring an input sample which is marked as a first class in the plurality of classes. The method further includes determining whether the input sample conforms to a first sample distribution corresponding to the first class. The method further includes identifying, in response to the input sample conforming to the first sample distribution, the input sample as a trusted sample. The trusted sample indicates that the input sample is correctly marked. By the method, a noise sample with a wrong label can be recognized, thus avoiding model degradation during update.
