Methods > General > Normalization

Adaptive Instance Normalization

Introduced by Huang et al. in Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

Adaptive Instance Normalization is a normalization method that aligns the mean and variance of the content features with those of the style features.

Instance Normalization normalizes the input to a single style specified by the affine parameters. Adaptive Instance Normaliation is an extension. In AdaIN, we receive a content input $x$ and a style input $y$, and we simply align the channel-wise mean and variance of $x$ to match those of $y$. Unlike Batch Normalization, Instance Normalization or Conditional Instance Normalization, AdaIN has no learnable affine parameters. Instead, it adaptively computes the affine parameters from the style input:

$$ \textrm{AdaIN}(x, y)= \sigma(y)\left(\frac{x-\mu(x)}{\sigma(x)}\right)+\mu(y) $$

Source: Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

Latest Papers

PAPER DATE
A Simple Baseline for StyleGAN Inversion
| Tianyi WeiDongdong ChenWenbo ZhouJing LiaoWeiming ZhangLu YuanGang HuaNenghai Yu
2021-04-15
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Haoyu ZhangMarcel GrimmerRaghavendra RamachandraKiran RajaChristoph Busch
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| Or PatashnikZongze WuEli ShechtmanDaniel Cohen-OrDani Lischinski
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Gihyun KwonJong Chul Ye
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GAN Inversion: A Survey
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Yuqian FuLi ZhangJunke WangYanwei FuYu-Gang Jiang
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Adversarial Latent Autoencoders
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Feature Quantization Improves GAN Training
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Knowledge distillation via adaptive instance normalization
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Remove Appearance Shift for Ultrasound Image Segmentation via Fast and Universal Style Transfer
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Unconstrained Facial Expression Transfer using Style-based Generator
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Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations
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What and Where to Translate: Local Mask-based Image-to-Image Translation
Wonwoong ChoSeunghwan ChoiJunwoo ParkDavid Keetae ParkTao QinJaegul Choo
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Emergence of Object Segmentation in Perturbed Generative Models
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Neural Comic Style Transfer: Case Study
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Towards Metamerism via Foveated Style Transfer
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Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
| Xun HuangSerge Belongie
2017-03-20

Tasks

TASK PAPERS SHARE
Image Generation 23 18.55%
Style Transfer 8 6.45%
Image-to-Image Translation 7 5.65%
Face Recognition 5 4.03%
Image Manipulation 5 4.03%
Image Super-Resolution 4 3.23%
Super-Resolution 4 3.23%
Denoising 4 3.23%
Face Generation 4 3.23%

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🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign

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