Task prediction method and device, equipment and storage medium

Abstract

The invention discloses a task prediction method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the steps of obtaining a training sample of a task prediction model, wherein the training sample comprises feature data of n modals of a sample individual; extracting feature representation information of the sample individual based on the feature data of the n modals through a task prediction model, and determining a task prediction result corresponding to the sample individual based on the feature representation information; determining model training loss based on the mutual information among the n modals and the accuracy of the task prediction result; and based on the model training loss, adjusting parameters of the task prediction model to obtain a trained task prediction model, the trained task prediction model being used for task prediction. In the embodiment of the invention, the task prediction model faces the feature data of multiple modals, more complete semantic information can be embedded in the task prediction process, and the accuracy of task prediction can be improved.

Description

technical field [0001] The embodiments of the present application relate to the technical field of artificial intelligence, and in particular to a task prediction method, device, equipment, and storage medium. Background technique [0002] Artificial Intelligence (AI) has been researched and applied in many fields, such as common smart homes, smart wearable devices, virtual assistants, smart speakers, smart marketing, unmanned driving, automatic driving, drones, Robots, smart medical care, smart customer service, etc. [0003] Machine learning (Machine Learning, ML) is the core of artificial intelligence and the fundamental way to make computers intelligent, and its application pervades all fields of artificial intelligence. Train the machine learning model through massive data, and perform the prediction of specific tasks through the trained machine learning model, which can assist or replace manual identification of specific tasks, and effectively avoid identification err...

AI Technical Summary

Problems solved by technology

[0004] However, since unimodal data usually cannot fully represent the semantics of individuals, the accuracy of task prediction performed on unimodal data will also be greatly reduced.

Images