3 citations · 3 across the 5 of their papers we have counts for
4 papers · 1 filter
Revisiting Latent Space of GAN Inversion for Real Image Editing
Kai Katsumata, Duc Minh Vo, Bei Liu +1
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 quali…
Soft Curriculum for Learning Conditional GANs with Noisy-Labeled and Uncurated Unlabeled Data
Kai Katsumata, Duc Minh Vo, Tatsuya Harada +1
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…
Balancing Reconstruction and Editing Quality of GAN Inversion for Real Image Editing with StyleGAN Prior Latent Space
Kai Katsumata, Duc Minh Vo, Bei Liu +1
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 quali…
OSSGAN: Open-Set Semi-Supervised Image Generation
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…