Migration retrieval method based on semi-supervised antagonistic generation network

Abstract

A migration retrieval method based on a semi-supervised countermeasure generation network is provided. A countermeasure generation network is designed to retrieve hashes across data domains, and the goal is to map the original and target datasets into a common Hamming space, so that the image retrieval in a particular scene can be migrated to a retrieval image of another scene through the learningof the semi-supervised antagonism generation network. Therefore, the problem that the unlabeled data can not be fully utilized and the retrieval model is only suitable for a single scene in the era of big data is solved. The invention effectively improves the automatic and intelligent level of image retrieval.

Description

technical field

[0001] The invention relates to the application of computer vision, pattern recognition, confrontation generation network, migration retrieval, cycle consistency, deep self-encoding convolutional neural network and deep learning technology in the field of image retrieval, especially a migration based on semi-supervised confrontation generation network Retrieval method. Background technique

[0002] In the web2.0 era, massive images, texts, and audio data are generated every day. How to quickly and accurately query the images that users need or are interested in in these vast and unlabeled data has become a research hotspot in the field of multimedia information retrieval. With the continuous development of artificial intelligence, the number of layers of convolutional neural network continues to deepen, regardless of image data, text data, audio data can be trained, so that it can learn a large amount of labeled data to accurately learn from input to output ...

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