A Cross-Domain Variational Adversarial Autoencoder Method

Abstract

The invention discloses a cross-domain variational confrontation self-encoding method, comprising the steps of: 1) using an encoder to decouple the content encoding and style encoding of cross-domain data; 2) using confrontation operations and variational operations to fit image Content coding and style coding; 3) Image reconstruction is achieved by splicing content coding and style coding, and a one-to-many continuous transformation of cross-domain images is obtained by cross-splicing content coding and style coding in different domains. The method of the invention realizes one-to-many continuous transformation of cross-domain images without providing any paired data.

Description

technical field [0001] The present invention relates to the technical field of computer vision, in particular to a cross-domain variational confrontation self-encoding method. Background technique [0002] In the field of computer vision, the use of single-domain data for image generation and image translation has achieved very good results. However, in real life and applications, these data usually come from different domains. For example, an object can have two representations of sketch and view, the same text content can be in different fonts, and so on. How to process cross-domain data is an important research direction. Existing cross-domain work mainly focuses on generating confrontation network GAN. This type of method achieves image generation by automatically fitting the posterior distribution through adversarial learning on data from different domains. In the learning process, pairs of data samples are always required, which has relatively high requirements for...

AI Technical Summary

Problems solved by technology

In the fields of industrial design and virtual reality, designers always hope to provide a picture to generate a series of continuously transformed pictures of the target domain. Existing methods cannot meet this demand.

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