Abstract: Disclosed is a method and device for processing data, and the method includes generating a target augmentation task sequence by processing the target data with a trained first model that performs inference on the target data to generate the target data augmentation task sequence, generate augmented target data by performing data augmentation on the target data according to the target augmentation task sequence, and obtaining a prediction result corresponding to the target data by inputting the augmented target data to a trained second model and performing a corresponding processing on the augmented target data by the trained second model.

Abstract: A dry battery electrode plate includes: a metal current collector and a self-supporting electrode film. The metal current collector is provided with pores. The self-supporting electrode film includes a first electrode film and a second electrode film. The first electrode film is arranged on one side of the metal current collector. The second electrode film is arranged on the other side of the metal current collector facing away from the first electrode film. The first electrode film and the second electrode film are configured to be press-fit connected by an external force. The first electrode film and the second electrode film are attached to the metal current collector. The first electrode film and the second electrode film are connected to each other at positions corresponding to the pores.

Abstract: A face recognition method includes generating a target face recognition model, performing face detection on an image to obtain a first face image, and performing face recognition on the first face image in the image by using the target face recognition model to obtain a first face feature. Generating the target face recognition model includes determining a training sample, the training sample comprising a training face image calibrated with identity information, and training a general face recognition model by using the training sample, and updating a parameter of the general face recognition model based on a training target to obtain the target face recognition model, the training target being a prediction result of the general face recognition model predicting identity information of a new training face image in the training sample to be the calibrated identity information of the training face image in the training sample.

Abstract: A face recognition method includes generating a target face recognition model, performing face detection on an image to obtain a first face image, and performing face recognition on the first face image in the image by using the target face recognition model to obtain a first face feature. Generating the target face recognition model includes determining a training sample, the training sample comprising a training face image calibrated with identity information, and training a general face recognition model by using the training sample, and updating a parameter of the general face recognition model based on a training target to obtain the target face recognition model, the training target being a prediction result of the general face recognition model predicting identity information of a new training face image in the training sample to be the calibrated identity information of the training face image in the training sample.