GANzilla: User-Driven Direction Discovery in Generative Adversarial Networks
Noyan Evirgen, Xiang 'Anthony' Chen
摘要
Figure 1: GANzilla is a tool that allows users to discover editing directions in Generative Adversarial Networks (GAN) via iterative scatter/gather interactions-a user-driven approach that complements many existing algorithm-driven methods. (a) A user starts by highlighting a region of interest (an optional step). (b) Based on the highlight (if there is), directions are sampled and clustered, each shown as an image edited by that direction. The user can gather clusters by selecting thumbnail images (indicated by a red border). (c) The user can see all the directions of the gathered clusters and (d) scatter them into new clusters. (e) The user can go back-and-forth across iterations to explore alternate choices of scatter/gather. (f) The user can test a selected direction (red border in c) on other images with individual sliders controlling the strength to apply the direction. (g) The user can bookmark directions that meet their editing goals.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper7
- Promptify: Text-to-Image Generation through Interactive Prompt Exploration with Large Language ModelsStephen Brade, Bryan Wang, Maurício Sousa, Sageev Oore 等UIST 2023 · 被引用 179 次
- PromptCharm: Text-to-Image Generation through Multi-modal Prompting and RefinementZhijie Wang, Yuheng Huang, Da Song, Lei Ma 等CHI 2024 · 被引用 111 次
- Prompting for Discovery: Flexible Sense-Making for AI Art-Making with DreamsheetsShm Garanganao Almeda, J. D. Zamfirescu-Pereira, Kyu Won Kim, Pradeep Mani Rathnam 等CHI 2024 · 被引用 46 次
- Concept Induction: Analyzing Unstructured Text with High-Level Concepts Using LLooMMichelle S. Lam, Janice Teoh, James A. Landay, Jeffrey Heer 等CHI 2024 · 被引用 46 次
- Augmenting Pathologists with NaviPath: Design and Evaluation of a Human-AI Collaborative Navigation SystemHongyan Gu, Chunxu Yang, Mohammad Haeri, Jing Wang 等CHI 2023 · 被引用 32 次
它引用的顶会 Paper16
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh 等ICML 2021 · 被引用 47,906 次
- StyleCLIP: Text-Driven Manipulation of StyleGAN ImageryOr Patashnik, Zongze Wu, Eli Shechtman, Daniel Cohen-Or 等ICCV 2021 · 被引用 1,437 次
- GANSpace: Discovering Interpretable GAN ControlsErik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, Sylvain ParisNeurIPS 2020 · 被引用 1,049 次
- Unsupervised Discovery of Interpretable Directions in the GAN Latent SpaceAndrey Voynov, Artem BabenkoICML 2020 · 被引用 459 次
- On the "steerability" of generative adversarial networksAli Jahanian, Lucy Chai, Phillip IsolaICLR 2020 · 被引用 421 次
相关 Paper
- GANravel: User-Driven Direction Disentanglement in Generative Adversarial NetworksNoyan Evirgen, Xiang Anthony ChenCHI 2023 · 被引用 15 次
- LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable DirectionsOguz Kaan Yüksel, Enis Simsar, Ezgi Gülperi Er, Pinar YanardagICCV 2021 · 被引用 71 次
- Navigating the GAN Parameter Space for Semantic Image EditingAnton Cherepkov, Andrey Voynov, Artem BabenkoCVPR 2021
- GANSpiration: Balancing Targeted and Serendipitous Inspiration in User Interface Design with Style-Based Generative Adversarial NetworkMohammad Amin Mozaffari, Xinyuan Zhang, Jinghui Cheng, Jin L. C. GuoCHI 2022 · 被引用 32 次
- Method for Exploring Generative Adversarial Networks (GANs) via Automatically Generated Image GalleriesEnhao Zhang, Nikola BanovicCHI 2021 · 被引用 28 次
