A Geometric Analysis of Deep Generative Image Models and Its Applications
By Binxu Wang, Carlos R. Ponce. 2021
42 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Generative Models
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Daniel Cohen-Or
- Eli Shechtman
- Luc Van Gool
- Alexei A. Efros
- Ming-Hsuan Yang
- Antonio Torralba
- Andrea Vedaldi
- Dacheng Tao
In Generative Models
- Daniel Cohen-Or
- Yuval Alaluf
- Eli Shechtman
- Rinon Gal
- Or Patashnik
- Dani Lischinski
- Amit H. Bermano
- Yotam Nitzan
Citations in the corpus
- StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery (2021, methodology): “Such methods can be categorized into: (i) methods that use image annotations to find meaningful latent paths [40, 1], and (ii) methods that find meaningful directions without supervision, and require manual annotation for each direction [13, 42, 48, 49].”
- Designing an encoder for StyleGAN image manipulation (2021, methodology): “Finally, several methods [14, 38, 39] find latent directions in an unsupervised manner and require manual annotations to determine the semantic meaning of each direction post hoc.”
- HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing (2021, background): “These range from full-supervision in the form of semantic labels [3, 16, 19, 56] and facial priors [59,60] to unsupervised approaches [24,57,63,64].”
- HyperInverter: Improving StyleGAN Inversion via Hypernetwork (2021, background): “As a result, other studies [16, 40, 46, 47] propose unsupervised approaches to accomplish the same aim without the need for manual annotations.”
- Pivotal Tuning for Latent-based Editing of Real Images (2021, methodology): “Some using fullsupervision in the form of semantic labels [10, 11, 34], others [15, 30, 35] find meaningful directions in a selfsupervised fashion, and finally recent works present unsupervised methods to achieve the same goal [14, 39, 40], requiring no manual annotations.”
- EditGAN: High-Precision Semantic Image Editing (2021, background): “Recently, deep generative models [4, 43, 44], in particular modern GANs [6–8, 45, 46], received much attention as a promising tool for efficient image editing, as it was found that latent space manipulations often lead to interpretable and predictable changes in output [24, 26, 27, 47–50].”