Exploring Intra-Class Variation Factors With Learnable Cluster Prompts for Semi-Supervised Image Synthesis

no code implementations • CVPR 2023 • Yunfei Zhang, Xiaoyang Huo, Tianyi Chen, Si Wu, Hau San Wong

Semi-supervised class-conditional image synthesis is typically performed by inferring and injecting class labels into a conditional Generative Adversarial Network (GAN).

Conditional Image Generation Generative Adversarial Network

SphericGAN: Semi-Supervised Hyper-Spherical Generative Adversarial Networks for Fine-Grained Image Synthesis

no code implementations • CVPR 2022 • Tianyi Chen, Yunfei Zhang, Xiaoyang Huo, Si Wu, Yong Xu, Hau San Wong

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.

Generative Adversarial Network Image Generation

Mask-Embedded Discriminator With Region-Based Semantic Regularization for Semi-Supervised Class-Conditional Image Synthesis

no code implementations • CVPR 2021 • Yi Liu, Xiaoyang Huo, Tianyi Chen, Xiangping Zeng, Si Wu, Zhiwen Yu, Hau-San Wong

Semi-supervised generative learning (SSGL) makes use of unlabeled data to achieve a trade-off between the data collection/annotation effort and generation performance, when adequate labeled data are not available.

Generative Adversarial Network Image Generation