METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR DATA DISTILLATION
Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for data distillation. The method includes: training an input data set by using a machine learning training process to establish a training model of the input data set; extracting multiple weights from the training model of the input data set, wherein the multiple weights contain information indicating the input data set, and the multiple weights are orthogonal to each other; and retraining the training model by using the multiple weights for generating a reconstructed data set. The embodiments of the present disclosure can greatly reduce the data storage cost of a data storage system and maintain the performance of the data storage system.
1 . A method for data distillation, comprising:
training an input data set by using a machine learning training process to establish a training model of the input data set;
extracting multiple weights from the training model of the input data set, wherein the multiple weights contain information indicating the input data set, and the multiple weights are orthogonal to each other; and
retraining the training model by using the multiple weights for generating a reconstructed data set.
2 . The method according to claim 1 , wherein retraining the training model by using the multiple weights comprises:
inputting random noise into the training model;
determining a loss function according to the random noise and the multiple weights; and
retraining the training model by using the loss function.
3 . The method according to claim 2 , further comprising:
inputting additional random noise into the retrained training model; and
executing the retrained training model to generate the reconstructed data set.
4 . The method according to claim 1 , wherein training the input data set by using the machine learning training process comprises:
performing feature extraction on the input data set by using a feature extraction loss function; and
classifying the input data set after feature extraction by using a classification loss function.
5 . The method according to claim 1 , wherein the machine learning training process comprises a convolutional neural network process and a fully connected layer process, and the input data set indicates an image.
6 . An electronic device, comprising:
at least one processing unit; and
at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, wherein the instructions, when executed by the at least one processing unit, cause the electronic device to perform actions comprising:
training an input data set by using a machine learning training process to establish a training model of the input data set;
extracting multiple weights from the training model of the input data set, wherein the multiple weights contain information indicating the input data set, and the multiple weights are orthogonal to each other; and
retraining the training model by using the multiple weights, for generating a reconstructed data set.
7 . The electronic device according to claim 6 , wherein retraining the training model by using the multiple weights comprises:
inputting random noise into the training model;
determining a loss function according to the random noise and the multiple weights; and
retraining the training model by using the loss function.
8 . The electronic device according to claim 7 , wherein the actions further comprise:
inputting additional random noise into the retrained training model; and
executing the retrained training model to generate the reconstructed data set.
9 . The electronic device according to claim 6 , wherein training the input data set by using the machine learning training process comprises:
performing feature extraction on the input data set by using a feature extraction loss function; and
classifying the input data set after feature extraction by using a classification loss function.
10 . The electronic device according to claim 6 , wherein the machine learning training process comprises a convolutional neural network process and a fully connected layer process, and the input data set indicates an image.
11 . A computer program product tangibly stored in a computer storage medium and comprising machine-executable instructions that, when executed by a device, cause the device to perform a method for data distillation, the method comprising:
training an input data set by using a machine learning training process to establish a training model of the input data set;
extracting multiple weights from the training model of the input data set, wherein the multiple weights contain information indicating the input data set, and the multiple weights are orthogonal to each other; and
retraining the training model by using the multiple weights for generating a reconstructed data set.
12 . The computer program product according to claim 11 , wherein retraining the training model by using the multiple weights comprises:
inputting random noise into the training model;
determining a loss function according to the random noise and the multiple weights; and
retraining the training model by using the loss function.
13 . The computer program product according to claim 12 , wherein the method further comprises:
inputting additional random noise into the retrained training model; and
executing the retrained training model to generate the reconstructed data set.
14 . The computer program product according to claim 11 , wherein training the input data set by using the machine learning training process comprises:
performing feature extraction on the input data set by using a feature extraction loss function; and
classifying the input data set after feature extraction by using a classification loss function.
15 . The computer program product according to claim 11 , wherein the machine learning training process comprises a convolutional neural network process and a fully connected layer process, and the input data set indicates an image.