A Style-Based Generator Architecture for Generative Adversarial Networks

CVPR 2019 • Tero Karras • Samuli Laine • Timo Aila

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 synthesis... (read more)

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Datasets

Introduced in the Paper:

FFHQ

Mentioned in the Paper:

LSUN CelebA-HQ Perceptual Similarity
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Image Generation CelebA-HQ 1024x1024 StyleGAN FID 5.06 # 1
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Image Generation FFHQ StyleGAN FID 4.43 # 4
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Image Generation LSUN Bedroom 256 x 256 StyleGAN FID 2.65 # 1
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Methods used in the Paper
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METHOD TYPE
Convolution
Convolutions
Adaptive Instance Normalization
Normalization
R1 Regularization
Regularization
Leaky ReLU
Activation Functions
Dense Connections
Feedforward Networks
Feedforward Network
Feedforward Networks
WGAN-GP Loss
Loss Functions
Adam
Stochastic Optimization
StyleGAN
Generative Models
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