The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation
Open accessAbstract
We explore and analyze the latent style space of Style-GAN2, a state-of-the-art architecture for image generation, using models pretrained on several different datasets. We first show that StyleSpace, the space of channel-wise style parameters, is significantly more disentangled than the other intermediate latent spaces explored by previous works. Next, we describe a method for discovering a large collection of style channels, each of which is shown to control a distinct visual attribute in a highly localized and dis-entangled manner. Third, we propose a simple method for identifying style channels that control a specific attribute, using a pretrained classifier or a small number of example images. Manipulation of visual attributes via these StyleSpace controls is shown to be better disentangled than via those proposed in previous works. To show this, we make use of a newly proposed Attribute Dependency metric. Finally, we demonstrate the applicability of StyleSpace controls to the manipulation of real images. Our findings pave the way to semantically meaningful and well-disentangled image manipulations via simple and intuitive interfaces.
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Cites
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
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Self-citationBatch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 14,214
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235
A Style-Based Generator Architecture for Generative Adversarial Networks
Karras, T., Laine, S., & Aila, T. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00453 · Cited by 11,714
Conditional Generative Adversarial Nets
Mirza, M., & Osindero, S. (2014) · arXiv · doi:10.48550/arxiv.1411.1784 · Cited by 8,863
Analyzing and Improving the Image Quality of StyleGAN
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., & Aila, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00813 · Cited by 6,332
Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization
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Large Scale GAN Training for High Fidelity Natural Image Synthesis
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BiSeNet: Bilateral Segmentation Network for Real-Time Semantic Segmentation
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Diffusion Autoencoders: Toward a Meaningful and Decodable Representation
Preechakul, K., Chatthee, N., Wizadwongsa, S., & Suwajanakorn, S. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01036 · Cited by 338
HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing
Alaluf, Y., Tov, O., Mokady, R., Gal, R., & Bermano, A. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01796 · Cited by 259
Face Generation and Editing With StyleGAN: A Survey
Melnik, A., Miasayedzenkau, M., Makaravets, D., Pirshtuk, D., Akbulut, E., Holzmann, D., Renusch, T., Reichert, G., & Ritter, H. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3350004 · Cited by 105
LAFITE: Towards Language-Free Training for Text-to-Image Generation
Zhou, Y., Zhang, R., Chen, C., Li, C., Tensmeyer, C., Yu, T., Gu, J., Xu, J., & Sun, T. (2021) · arXiv · doi:10.48550/arxiv.2111.13792 · Cited by 38
DEEPFAKER: A Unified Evaluation Platform for Facial Deepfake and Detection Models
Wang, L., Meng, X., Li, D., Zhang, X., Ji, S., & Guo, S. (2024) · ACM Transactions on Privacy and Security · doi:10.1145/3634914 · Cited by 11
Metadata
- Authors
- Wu, Z., Lischinski, D., & Shechtman, E.
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 12858-12867
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3173241699
- Cited by
- 389
- References
- 38
- FWCI
- 28.42
- Type
- conference paper
- Language
- en
- Open access
- green
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