7 citations · 8 across the 4 of their papers we have counts for
4 papers
Through Van Gogh's Eyes: Global Style Transfer with Diffusion Model
Jeongha Lee, Yujin Kim, Ghazanfar Ali +2
Artistic image synthesis aims to recreate the expressive visual identity of a target artist, yet existing methods often fail to capture an artist's global style. Conventional style…
When Model Knowledge meets Diffusion Model: Diffusion-assisted Data-free Image Synthesis with Alignment of Domain and Class
Yujin Kim, Hyunsoo Kim, Hyunwoo J. Kim +1
Open-source pre-trained models hold great potential for diverse applications, but their utility declines when their training data is unavailable. Data-Free Image Synthesis (DFIS) a…
NaturalInversion: Data-Free Image Synthesis Improving Real-World Consistency
Yujin Kim, Dogyun Park, Dohee Kim +1
We introduce NaturalInversion, a novel model inversion-based method to synthesize images that agrees well with the original data distribution without using real data. In NaturalInv…
How to Fine-tune Models with Few Samples: Update, Data Augmentation, and Test-time Augmentation
Yujin Kim, Jaehoon Oh, Sungnyun Kim +1
Most of the recent few-shot learning (FSL) algorithms are based on transfer learning, where a model is pre-trained using a large amount of source data, and the pre-trained model is…