Pegatron Corporation

Shih-Jen Chu

A model training method and a model training system are disclosed. The method includes the following. In a first iteration training, first training data corresponding to a first sub-task and second training data corresponding to a second sub-task are input to a target model. A first and a second image identification rate of the target model with respect to the first and the second sub-task are evaluated respectively according to a first output corresponding to the first sub-task and a second output corresponding to the second sub-task from the target model. A first and a second sampling rate respectively corresponding to the first and the second training data in a second iteration training are adjusted according to the first and the second image identification rate. The first and the second sampling rate are respectively negatively correlated to the first and the second image identification rate.

Model training method and model training system

A training method for an anomaly detection model and an electronic device using the same are provided. The anomaly detection model includes a generative model and a discriminative model. The training method for the anomaly detection model includes the following steps. One of a plurality of original images and one of a plurality of task information are used as a training sample. The training sample is input to the generative model and the discriminative model to calculate a plurality of network loss results corresponding to the training sample. If the original image of the training sample does not match the task information of the training sample, a first loss function is obtained based on a weighted sum of reciprocals of the network loss results, and the generative model is trained according to the first loss function.

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Taipei, Taiwan

Shih-Jen Chu