Semi-Supervised Semantic Image Segmentation by Deep Diffusion Models and Generative Adversarial Networks

被引:0
|
作者
Diaz-Frances, Jose Angel [1 ]
Fernandez-Rodriguez, Jose David [1 ]
Thurnhofer-Hemsi, Karl [1 ]
Lopez-Rubio, Ezequiel [1 ]
机构
[1] Univ Malaga, ITIS Software, Calle Arquitecto Francisco Penalosa 18, Malaga 29010, Spain
关键词
Semantic segmentation; semi-supervised; diffusion model;
D O I
10.1142/S0129065724500576
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Typically, deep learning models for image segmentation tasks are trained using large datasets of images annotated at the pixel level, which can be expensive and highly time-consuming. A way to reduce the amount of annotated images required for training is to adopt a semi-supervised approach. In this regard, generative deep learning models, concretely Generative Adversarial Networks (GANs), have been adapted to semi-supervised training of segmentation tasks. This work proposes MaskGDM, a deep learning architecture combining some ideas from EditGAN, a GAN that jointly models images and their segmentations, together with a generative diffusion model. With careful integration, we find that using a generative diffusion model can improve EditGAN performance results in multiple segmentation datasets, both multi-class and with binary labels. According to the quantitative results obtained, the proposed model improves multi-class image segmentation when compared to the EditGAN and DatasetGAN models, respectively, by 4.5% and 5.0%. Moreover, using the ISIC dataset, our proposal improves the results from other models by up to 11% for the binary image segmentation approach.
引用
收藏
页数:16
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