Method and device for predicting beauty based on migration and weak supervision, and storage medium

A device for predicting beauty based on migration and weak supervision, comprising a processor and a memory connected with the processor, wherein the memory is configured to store an executable instruction which, when executed by the processor, causes the processor to execute steps of:

• preprocessing an inputted face image to obtain a preprocessed image;
• training a source domain network by using the preprocessed image, and migrating a parameter of the trained source domain network to a target domain network, wherein in the migrating process, for the source domain network, a loss function of the source domain network is obtained according to an output of a softmax layer of the source domain network with a T value greater than 1 and an original label;
• for the target domain network, a first sub-loss function of the target domain network is obtained according to an output of a softmax layer of the target domain network with a T value greater than 1 and the original label, a second sub-loss function of the target domain network is obtained according to the output of the softmax layer of the target domain network with the T value equal to 1 and the original label, and the first sub-loss function and the second sub-loss function are added to obtain a loss function of the target domain network;
• classifying the preprocessed image into a noise image marked with a noise label and a truth-value image marked with a truth-value label, and inputting the noise image and the truth-value image into the target domain network to obtain an image feature;
• and
• inputting the image feature into a residual network to learn mapping from the image feature to a difference value between the noise label and the truth-value label to obtain a first predicted value, inputting the image feature into a standard neural network to learn mapping from the image feature to the truth-value label to obtain a second predicted value, adding the first predicted value and the second predicted value and then inputting the added value into a first classifier to obtain a first face beauty prediction result, inputting the second predicted value into a second classifier to obtain a second face beauty prediction result, and obtaining a final face beauty prediction result according to the first face beauty prediction result and the second face beauty prediction result.

A non-transitory storage medium storing an executable instruction which, when executed by a computer, causes the computer to execute steps of:

• preprocessing an inputted face image to obtain a preprocessed image;
• training a source domain network by using the preprocessed image, and migrating a parameter of the trained source domain network to a target domain network, wherein in the migrating process, for the source domain network, a loss function of the source domain network is obtained according to an output of a softmax layer of the source domain network with a T value greater than 1 and an original label;
• for the target domain network, a first sub-loss function of the target domain network is obtained according to an output of a softmax layer of the target domain network with a T value greater than 1 and the original label, a second sub-loss function of the target domain network is obtained according to the output of the softmax layer of the target domain network with the T value equal to 1 and the original label, and the first sub-loss function and the second sub-loss function are added to obtain a loss function of the target domain network;
• classifying the preprocessed image into a noise image marked with a noise label and a truth-value image marked with a truth-value label, and inputting the noise image and the truth-value image into the target domain network to obtain an image feature;
• and
• inputting the image feature into a residual network to learn mapping from the image feature to a difference value between the noise label and the truth-value label to obtain a first predicted value, inputting the image feature into a standard neural network to learn mapping from the image feature to the truth-value label to obtain a second predicted value, adding the first predicted value and the second predicted value and then inputting the added value into a first classifier to obtain a first face beauty prediction result, inputting the second predicted value into a second classifier to obtain a second face beauty prediction result, and obtaining a final face beauty prediction result according to the first face beauty prediction result and the second face beauty prediction result.