A method includes: performing a plurality of times of first clustering processing on a plurality of training images based on a plurality of target features to obtain a plurality of first clustering results, where each of the plurality of first clustering results corresponds to one silhouette coefficient, and the silhouette coefficient indicates cluster quality; determining a first target clustering result based on the silhouette coefficients, where the first target clustering result includes M clustering categories; and performing second clustering processing on the plurality of training images based on the plurality of target features to obtain a second clustering result, where a quantity of clustering categories included in the second clustering result is M.

A method includes: performing a plurality of times of first clustering processing on a plurality of training images based on a plurality of target features to obtain a plurality of first clustering results, where each of the plurality of first clustering results corresponds to one silhouette coefficient, and the silhouette coefficient indicates cluster quality; determining a first target clustering result based on the silhouette coefficients, where the first target clustering result includes M clustering categories; and performing second clustering processing on the plurality of training images based on the plurality of target features to obtain a second clustering result, where a quantity of clustering categories included in the second clustering result is M.

The present disclosure provides a multi-task deep Hash learning-based retrieval method for massive logistics product images. According to the idea of multi-tasking, Hash codes of a plurality of lengths can be learned simultaneously as high-level image representation. Compared with single-tasking in the prior art, the method overcomes shortcomings such as waste of hardware resources and high time cost caused by model retraining under single-tasking. Compared with the traditional idea of learning a single Hash code as an image representation and using it for retrieval, information association among Hash codes of a plurality of lengths is mined, and the mutual information loss is designed to improve the representational capacity of the Hash codes, which addresses the poor representational capacity of a single Hash code, and thus improves the retrieval performance of Hash codes.

The present disclosure provides a multi-task deep Hash learning-based retrieval method for massive logistics product images. According to the idea of multi-tasking, Hash codes of a plurality of lengths can be learned simultaneously as high-level image representation. Compared with single-tasking in the prior art, the method overcomes shortcomings such as waste of hardware resources and high time cost caused by model retraining under single-tasking. Compared with the traditional idea of learning a single Hash code as an image representation and using it for retrieval, information association among Hash codes of a plurality of lengths is mined, and the mutual information loss is designed to improve the representational capacity of the Hash codes, which addresses the poor representational capacity of a single Hash code, and thus improves the retrieval performance of Hash codes.

An analysis device includes: a memory; and a processor coupled to the memory and configured to: execute a first learning process on a generative model for images such that the images that bring a recognition result of an image recognition process into a preassigned state are generated; execute a second learning process on the generative model on which the first learning process which has been executed such that recognition accuracy of the images generated by the generative model on which the first learning process has been executed matches desired recognition accuracy; acquire information on back-error propagation calculated by executing the image recognition process, for the images with the desired recognition accuracy generated by executing the second learning process; and generate evaluation information that indicates image parts that cause over-detection at the desired recognition accuracy, based on the acquired information on the back-error propagation.

An analysis device includes: a memory; and a processor coupled to the memory and configured to: execute a first learning process on a generative model for images such that the images that bring a recognition result of an image recognition process into a preassigned state are generated; execute a second learning process on the generative model on which the first learning process which has been executed such that recognition accuracy of the images generated by the generative model on which the first learning process has been executed matches desired recognition accuracy; acquire information on back-error propagation calculated by executing the image recognition process, for the images with the desired recognition accuracy generated by executing the second learning process; and generate evaluation information that indicates image parts that cause over-detection at the desired recognition accuracy, based on the acquired information on the back-error propagation.

A method and apparatus for training a virtual image generating model, a method and apparatus for generating a virtual image, a device, a storage medium, and a computer program product are provided. The method for training a virtual image generating model includes: training a first initial model using a standard image sample set and a random vector sample set as first of sample data, to obtain an image generating model; training a second initial model using a test latent vector sample set and a test image sample set as second sample data, to obtain an image encoding model; training a third initial model using a standard image sample set and a descriptive text sample set as third sample data, to obtain an image editing model; and training a fourth initial model using the third sample data based on the above models, to obtain the virtual image generating model.

A method and apparatus for training a virtual image generating model, a method and apparatus for generating a virtual image, a device, a storage medium, and a computer program product are provided. The method for training a virtual image generating model includes: training a first initial model using a standard image sample set and a random vector sample set as first of sample data, to obtain an image generating model; training a second initial model using a test latent vector sample set and a test image sample set as second sample data, to obtain an image encoding model; training a third initial model using a standard image sample set and a descriptive text sample set as third sample data, to obtain an image editing model; and training a fourth initial model using the third sample data based on the above models, to obtain the virtual image generating model.

A method for analyzing experimental data related to quantum system by using neural network by an electronic device is provided. The method includes: generating a training dataset according to experimental data; performing one or more filtering operations on the training dataset to generate one or more filtered training datasets respectively corresponding to the filtering operations; training a first neural network and a second neural network by inputting the original and filtered training datasets; evaluating the first and the second neural network; obtaining one or more classification accuracies of the first and the second neural network; identifying the differences between pairs of classification accuracies; and determining impact level of each information preserved or removed by each of the filtering operations according to the differences.

A method for analyzing experimental data related to quantum system by using neural network by an electronic device is provided. The method includes: generating a training dataset according to experimental data; performing one or more filtering operations on the training dataset to generate one or more filtered training datasets respectively corresponding to the filtering operations; training a first neural network and a second neural network by inputting the original and filtered training datasets; evaluating the first and the second neural network; obtaining one or more classification accuracies of the first and the second neural network; identifying the differences between pairs of classification accuracies; and determining impact level of each information preserved or removed by each of the filtering operations according to the differences.