Abstract: A model determination apparatus is configured to execute the following operations. Multiple candidate models are validated based on first adversarial validation data to generate a first accuracy corresponding to each of the candidate models, wherein the first adversarial validation data is generated by a first adversarial attack adjustment performed on validation data based on an initial model. A second adversarial attack adjustment is performed on the validation data based on each of the candidate models to generate a plurality of second adversarial validation data corresponding to each of the candidate models respectively. The candidate models are validated based on the corresponding second adversarial validation data to generate a second accuracy corresponding to each of the candidate models. At least one output model is selected from the candidate models based on the first accuracy and the second accuracy corresponding to each of the candidate models.

Abstract: A model generating apparatus and method are provided. The apparatus receives a plurality of sample images. The apparatus generates a plurality of adversarial samples corresponding to the sample images. The apparatus inputs the sample images and the adversarial samples respectively to a first encoder and a second encoder in a self-supervised neural network to generate a plurality of first feature extractions and a plurality of second feature extractions. The apparatus calculates a similarity of each of the first feature extractions and the second feature extractions to train the self-supervised neural network. The apparatus generates a task model based on the first encoder and a plurality of labeled data.