1 code implementation • ICCV 2023 • Sheng-Yu Wang, Alexei A. Efros, Jun-Yan Zhu, Richard Zhang
The problem of data attribution in such models -- which of the images in the training set are most responsible for the appearance of a given generated image -- is a difficult yet important one.
1 code implementation • ICCV 2023 • Nupur Kumari, Bingliang Zhang, Sheng-Yu Wang, Eli Shechtman, Richard Zhang, Jun-Yan Zhu
To achieve this goal, we propose an efficient method of ablating concepts in the pretrained model, i. e., preventing the generation of a target concept.
1 code implementation • 6 Oct 2022 • Daohan Lu, Sheng-Yu Wang, Nupur Kumari, Rohan Agarwal, Mia Tang, David Bau, Jun-Yan Zhu
To address this need, we introduce the task of content-based model search: given a query and a large set of generative models, finding the models that best match the query.
Ranked #1 on Model Description Based Search on Generative Models
Contrastive Learning Image and Sketch based Model Retrieval +4
1 code implementation • 28 Jul 2022 • Sheng-Yu Wang, David Bau, Jun-Yan Zhu
Our method allows a user to create a model that synthesizes endless objects with defined geometric changes, enabling the creation of a new generative model without the burden of curating a large-scale dataset.
1 code implementation • ICCV 2021 • Sheng-Yu Wang, David Bau, Jun-Yan Zhu
In particular, we change the weights of an original GAN model according to user sketches.
4 code implementations • CVPR 2020 • Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, Alexei A. Efros
In this work we ask whether it is possible to create a "universal" detector for telling apart real images from these generated by a CNN, regardless of architecture or dataset used.
2 code implementations • ICCV 2019 • Sheng-Yu Wang, Oliver Wang, Andrew Owens, Richard Zhang, Alexei A. Efros
Most malicious photo manipulations are created using standard image editing tools, such as Adobe Photoshop.