Fetching the paper…
Reading the bibliography…
Inspired by the ability of StyleGAN to generate highly realistic images in a variety of domains, much recent work has focused on understanding how to use the latent spaces of StyleGAN to manipulate generated and real images.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
Earlier work this paper cites.
Deep learning face attributes in the wild, 2015
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Earlier work this paper cites.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao · 2015
Earlier work this paper cites.
Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
Earlier work this paper cites.
Generative adversarial text to image synthesis
S. Reed, Zeynep Akata, Xinchen Yan, L. Logeswaran, B. Schiele, and H. Lee · 2016
Earlier work this paper cites.
Semantic image synthesis via adversarial learning
H. Dong, Simiao Yu, Chao Wu, and Y. Guo · 2017
Earlier work this paper cites.
Progressive growing of GANs for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
StackGAN: Text to photo-realistic image synthesis with stacked generative adversarial networks
Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, and Dimitris N Metaxas · 2017
Earlier work this paper cites.
Text-adaptive generative adversarial networks: Manipulating images with natural language
Seonghyeon Nam, Yunji Kim, and S. Kim · 2018
Earlier work this paper cites.
AttnGAN: Fine-grained text to image generation with attentional generative adversarial networks
T. Xu, Pengchuan Zhang, Qiuyuan Huang, Han Zhang, Zhe Gan, Xiaolei Huang, and X. He · 2018
Earlier work this paper cites.
Arcface: Additive angular margin loss for deep face recognition
Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou · 2019
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Earlier work this paper cites.
A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
Earlier work this paper cites.
Visualbert: A simple and performant baseline for vision and language
Liunian Harold Li, Mark Yatskar, Da Yin, C. Hsieh, and Kai-Wei Chang · 2019
Earlier work this paper cites.
Object-driven text-to-image synthesis via adversarial training
Wenbo Li, Pengchuan Zhang, Lei Zhang, Qiuyuan Huang, X. He, Siwei Lyu, and Jianfeng Gao · 2019
Earlier work this paper cites.
Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Jiasen Lu, Dhruv Batra, D. Parikh, and Stefan Lee · 2019
Earlier work this paper cites.
LXMERT: Learning cross-modality encoder representations from transformers
Hao Hao Tan and Mohit Bansal · 2019
Earlier work this paper cites.
StackGAN++: Realistic image synthesis with stacked generative adversarial networks
Han Zhang, T. Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, and Dimitris N. Metaxas · 2019
Cited alongside, same era.
Rameen Abdal, Peihao Zhu, Niloy Mitra, and Peter Wonka · 2020
Cited alongside, same era.
Language models are few-shot learners
T. Brown, B. Mann, Nick Ryder, Melanie Subbiah, J. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, G. Krüger, T. Henighan, R. Child, Aditya Ramesh, D. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, E. Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, J. Clark, Christopher Berner, Sam McCandlish, A. Radford, Ilya Sutskever, and Dario Amodei · 2020
Cited alongside, same era.
Uniter: Universal image-text representation learning
Yen-Chun Chen, Linjie Li, Licheng Yu, A. E. Kholy, Faisal Ahmed, Zhe Gan, Y. Cheng, and Jing jing Liu · 2020
Cited alongside, same era.
Learning visual representations with caption annotations
Mert Bulent Sariyildiz, Julien Perez, and Diane Larlus · 2020
Later among the works it cites.
Interpreting the latent space of GANs for semantic face editing
Yujun Shen, Jinjin Gu, Xiaoou Tang, and Bolei Zhou · 2020
Later among the works it cites.
InterFaceGAN: interpreting the disentangled face representation learned by GANs
Yujun Shen, Ceyuan Yang, Xiaoou Tang, and Bolei Zhou · 2020
Later among the works it cites.
Closed-form factorization of latent semantics in GANs
Yujun Shen and Bolei Zhou · 2020
Later among the works it cites.
VL-BERT: Pre-training of generic visual-linguistic representations
Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
StarGAN v2: Diverse image synthesis for multiple domains
Yunjey Choi, Youngjung Uh, Jaejun Yoo, and Jung-Woo Ha · 2020
Cited alongside, same era.
Editing in style: Uncovering the local semantics of GANs
Edo Collins, Raja Bala, Bob Price, and Sabine Süsstrunk · 2020
Cited alongside, same era.
VirTex: Learning visual representations from textual annotations
Karan Desai and J. Johnson · 2020
Cited alongside, same era.
GANSpace: Discovering interpretable GAN controls
Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, and Sylvain Paris · 2020
Cited alongside, same era.
Training generative adversarial networks with limited data
Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2020
Cited alongside, same era.
Training generative adversarial networks with limited data
Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2020
Cited alongside, same era.
Analyzing and improving the image quality of StyleGAN
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
Cited alongside, same era.
Text-to-image generation grounded by fine-grained user attention
J. Y. Koh, Jason Baldridge, H. Lee, and Yinfei Yang · 2020
Cited alongside, same era.
StyleRig: Rigging StyleGAN for 3d control over portrait images
Ayush Tewari, Mohamed Elgharib, Gaurav Bharaj, Florian Bernard, Hans-Peter Seidel, Patrick Pérez, Michael Zollhöfer, and Christian Theobalt · 2020
Later among the works it cites.
Unsupervised discovery of interpretable directions in the GAN latent space
Andrey Voynov and Artem Babenko · 2020
Later among the works it cites.
StyleSpace analysis: Disentangled controls for StyleGAN image generation
Zongze Wu, Dani Lischinski, and Eli Shechtman · 2020
Later among the works it cites.
TediGAN: Text-guided diverse face image generation and manipulation
Weihao Xia, Yujiu Yang, Jing-Hao Xue, and Baoyuan Wu · 2020
Later among the works it cites.
Only a matter of style: Age transformation using a style-based regression model
Yuval Alaluf, Or Patashnik, and Daniel Cohen-Or · 2021
Closest in time.
Multimodal neurons in artificial neural networks
Gabriel Goh, Nick Cammarata, Chelsea Voss, Shan Carter, Michael Petrov, Ludwig Schubert, Alec Radford, and Chris Olah · 2021
Closest in time.
VOGUE: Try-on by StyleGAN interpolation optimization
Kathleen M Lewis, Srivatsan Varadharajan, and Ira Kemelmacher-Shlizerman · 2021
Closest in time.
Generating images from prompts using CLIP and StyleGAN
Victor Perez · 2021
Closest in time.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
Closest in time.
DALL·E: Creating Images from Text
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, and Scott Gray · 2021
Closest in time.
Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
Closest in time.
Designing an encoder for stylegan image manipulation
Omer Tov, Yuval Alaluf, Yotam Nitzan, Or Patashnik, and Daniel Cohen-Or · 2021
Closest in time.
A geometric analysis of deep generative image models and its applications
Binxu Wang and Carlos R Ponce · 2021
Closest in time.