173 citations · 551 across the 20 of their papers we have counts for
23 papers
TopP&R: Robust Support Estimation Approach for Evaluating Fidelity and Diversity in Generative Models
Pum Jun Kim, Yoojin Jang, Jisu Kim +1
We propose a robust and reliable evaluation metric for generative models by introducing topological and statistical treatments for rigorous support estimation. Existing metrics, su…
Efficient Storage of Fine-Tuned Models via Low-Rank Approximation of Weight Residuals
Simo Ryu, Seunghyun Seo, Jaejun Yoo
In this paper, we present an efficient method for storing fine-tuned models by leveraging the low-rank properties of weight residuals. Our key observation is that weight residuals…
Fix the Noise: Disentangling Source Feature for Controllable Domain Translation
Dongyeun Lee, Jae Young Lee, Doyeon Kim +3
Recent studies show strong generative performance in domain translation especially by using transfer learning techniques on the unconditional generator. However, the control betwee…
Can We Find Strong Lottery Tickets in Generative Models?
Sangyeop Yeo, Yoojin Jang, Jy-yong Sohn +2
Yes. In this paper, we investigate strong lottery tickets in generative models, the subnetworks that achieve good generative performance without any weight update. Neural network p…
LANIT: Language-Driven Image-to-Image Translation for Unlabeled Data
Jihye Park, Sunwoo Kim, Soohyun Kim +4
Existing techniques for image-to-image translation commonly have suffered from two critical problems: heavy reliance on per-sample domain annotation and/or inability of handling mu…
Rethinking the Truly Unsupervised Image-to-Image Translation
Kyungjune Baek, Yunjey Choi, Youngjung Uh +2
Every recent image-to-image translation model inherently requires either image-level (i.e. input-output pairs) or set-level (i.e. domain labels) supervision. However, even set-leve…