Paper Info
Title
SphericGAN: Semi-supervised Hyper-spherical Generative Adversarial Networks for Fine-grained Image Synthesis
Abstract
Generative Adversarial Network (GAN)-based models have greatly facilitated image synthesis. However, the model performance may be degraded when applied to finegrained data, due to limited training samples and subtle distinction among categories. Different from generic GAN-s, we address the issue from a new perspective of discovering and utilizing the underlying structure of real data to explicitly regularize the spatial organization of latent space. To reduce the dependence of generative models on labeled data, we propose a semi-supervised hyper-spherical GAN for class-conditional fine-grained image generation, and our model is referred to as SphericGAN. By projecting random vectors drawn from a prior distribution onto a hyper-sphere, we can model more complex distributions, while at the same time the similarity between the resulting latent vectors depends only on the angle, but not on their magnitudes. On the other hand, we also incorporate a mapping network to map real images onto the hyper-sphere, and match latent vectors with the underlying structure of real data via real-fake cluster alignment. As a result, we obtain a spatially organized latent space, which is useful for capturing class-independent variation factors. The experi-mental results suggest that our SphericGAN achieves state-of-the-art performance in synthesizing high-fidelity images with precise class semantics.
Year
DOI
Venue
2022
10.1109/CVPR52688.2022.00976
IEEE Conference on Computer Vision and Pattern Recognition
Keywords
DocType
Volume
Self-& semi-& meta- Image and video synthesis and generation
Conference
2022
Issue
Citations 
PageRank 
1
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Tianyi Chen101.35
Yunfei Zhang200.68
Xiaoyang Huo301.69
Si Wu414816.73
Yong Xu559537.87
Hau San Wong600.34