Object-stable unsupervised dual contrastive learning image-to-image translation with query-selected attention and convolutional block attention module.

ABSTRACT: Recently, contrastive learning has gained popularity in the field of unsupervised image-to-image (I2I) translation. In a previous study, a query-selected attention (QS-Attn) module, which employed an attention matrix with a probability distribution, was used to maximize the mutual information between the source and translated images. This module selected significant queries using an entropy metric computed from the attention matrix. However, it often selected many queries with equal significance measures, leading to an excessive focus on the background. In this study, we proposed a dual-learning framework with QS-Attn and convolutional block attention module (CBAM) called object-stable dual contrastive learning generative adversarial network (OS-DCLGAN). In this paper, we utilize a CBAM, w

SUBMITTER: Oh Y 

PROVIDER: S-EPMC10627467 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature