In the field of generative artificial intelligence,the research of disentangled representation learning further promotes the development of image generation methods.However,existing disentanglement methods pay more attention to low-dimensional representation of image generation,ignoring inherent interpretable factors of the target variation image.This oversight results in generated image being susceptible to the influence of other irrelevant attribute features.To address this issue,an image generation method for cognizing image attribute features from the perspective of disentangled representation learning is proposed.Firstly,candidate traversal directions for the target variation image are obtained by training,starting from the latent space of the generative model.Secondly,an unsupervised semantic decomposition strategy is constructed,and the interpretable directions embedded in the latent space are jointly discovered based on the direction of candidate traversals.Finally,a contrast simulator and a variation space are constructed using disentangled encoders and contrastive learning.Consequently,the disentangled representations of the target variation image are extracted from the interpretable directions and the image is generated.Extensive experiments on five popular disentanglement datasets demonstrate the superior performance of the proposed method.
关键词
解耦表征学习/潜在空间/可解释方向/图像生成/变化空间
Key words
Disentangled Representation Learning/Latent Space/Interpretable Direction/Image Ge-neration/Variation Space
维普
万方数据