This document discusses style-based generative adversarial networks and techniques used in them. It introduces adaptive instance normalization (AdaIN) which aligns the mean and variance of features to match a target style. It also discusses mixing regularization which combines styles at the latent space level and perceptual path length which measures diversity of generated images.

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DEEP LEARNING JP
[DL Papers]
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A Style-Based Generator Architecture for Generative
Adversarial Networks

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X. Huang et al. “Arbitrary StyleTransfer in Real-time with Adaptive Instance Normalization”, 2017.

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