no code implementations • 18 Jul 2023 • Kai Katsumata, Duc Minh Vo, Bei Liu, Hideki Nakayama
The exploration of the latent space in StyleGANs and GAN inversion exemplify impressive real-world image editing, yet the trade-off between reconstruction quality and editing quality remains an open problem.
no code implementations • 17 Jul 2023 • Kai Katsumata, Duc Minh Vo, Tatsuya Harada, Hideki Nakayama
Label-noise or curated unlabeled data is used to compensate for the assumption of clean labeled data in training the conditional generative adversarial network; however, satisfying such an extended assumption is occasionally laborious or impractical.
no code implementations • 31 May 2023 • Kai Katsumata, Duc Minh Vo, Bei Liu, Hideki Nakayama
The exploration of the latent space in StyleGANs and GAN inversion exemplify impressive real-world image editing, yet the trade-off between reconstruction quality and editing quality remains an open problem.
1 code implementation • CVPR 2022 • Kai Katsumata, Duc Minh Vo, Hideki Nakayama
We introduce a challenging training scheme of conditional GANs, called open-set semi-supervised image generation, where the training dataset consists of two parts: (i) labeled data and (ii) unlabeled data with samples belonging to one of the labeled data classes, namely, a closed-set, and samples not belonging to any of the labeled data classes, namely, an open-set.