A Style-Based Generator Architecture for Generative Adversarial Networks
Tero Karras, Samuli Laine, Timo Aila
Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.
We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synt ...
hesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces.
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We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature.
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Research context
765
Citations
113
References
Tasks
Generator (circuit theory), Computer science, Interpolation (computer graphics), Generative grammar, Identity (music), Variation (astronomy), Quality (philosophy), Key (lock)
Methods
Architecture
Domains
Artificial intelligence, Machine learning, Computer Vision and Pattern Recognition
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