Instance Normalization Explained

Instance Normalization

Introduced by Ulyanov et al. in Instance Normalization: The Missing Ingredient for Fast Stylization

Instance Normalization (also known as contrast normalization) is a normalization layer where:

$$ y_{tijk} = \frac{x_{tijk} - \mu_{ti}}{\sqrt{\sigma_{ti}^2 + \epsilon}}, \quad \mu_{ti} = \frac{1}{HW}\sum_{l=1}^W \sum_{m=1}^H x_{tilm}, \quad \sigma_{ti}^2 = \frac{1}{HW}\sum_{l=1}^W \sum_{m=1}^H (x_{tilm} - mu_{ti})^2. $$

This prevents instance-specific mean and covariance shift simplifying the learning process. Intuitively, the normalization process allows to remove instance-specific contrast information from the content image in a task like image stylization, which simplifies generation.

Source: Instance Normalization: The Missing Ingredient for Fast Stylization

Paper Code Snippet

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Mix and match networks: encoder-decoder alignment for zero-pair image translation \| Yaxing Wang • Joost van de Weijer • Luis Herranz	2018-04-06
High-quality nonparallel voice conversion based on cycle-consistent adversarial network Fuming Fang • Junichi Yamagishi • Isao Echizen • Jaime Lorenzo-Trueba	2018-04-02
Cross-modality image synthesis from unpaired data using CycleGAN: Effects of gradient consistency loss and training data size Yuta Hiasa • Yoshito Otake • Masaki Takao • Takumi Matsuoka • Kazuma Takashima • Jerry L. Prince • Nobuhiko Sugano • Yoshinobu Sato	2018-03-18
Gotta Adapt 'Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the Wild Luan Tran • Kihyuk Sohn • Xiang Yu • Xiaoming Liu • Manmohan Chandraker	2018-02-28
Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data \| Amjad Almahairi • Sai Rajeswar • Alessandro Sordoni • Philip Bachman • Aaron Courville	2018-02-27
Invertible Autoencoder for domain adaptation Yunfei Teng • Anna Choromanska • Mariusz Bojarski	2018-02-10
Unsupervised Cipher Cracking Using Discrete GANs \| Aidan N. Gomez • Sicong Huang • Ivan Zhang • Bryan M. Li • Muhammad Osama • Lukasz Kaiser	2018-01-15
The Information-Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Modeling Shengjia Zhao • Jiaming Song • Stefano Ermon	2018-01-01
ComboGAN: Unrestrained Scalability for Image Domain Translation \| Asha Anoosheh • Eirikur Agustsson • Radu Timofte • Luc Van Gool # 5 FACIAL EXPRESSION TRANSLATION ON CELEBA	2017-12-19
CycleGAN, a Master of Steganography Casey Chu • Andrey Zhmoginov • Mark Sandler	2017-12-08
Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks \| Takuhiro Kaneko • Hirokazu Kameoka	2017-11-30
Unpaired Photo-to-Caricature Translation on Faces in the Wild \| Ziqiang Zheng • Wang Chao • Zhibin Yu • Nan Wang • Haiyong Zheng • Bing Zheng	2017-11-29
Camera Style Adaptation for Person Re-identification \| Zhun Zhong • Liang Zheng • Zhedong Zheng • Shaozi Li • Yi Yang # 32 PERSON RE-IDENTIFICATION ON DUKEMTMC-REID	2017-11-28
Adversarial Transfer Learning for Cross-domain Visual Recognition Shanshan Wang • Lei Zhang • JingRu Fu	2017-11-24
Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification Weijian Deng • Liang Zheng • Qixiang Ye • Guoliang Kang • Yi Yang • Jianbin Jiao # 2 UNSUPERVISED PERSON RE-IDENTIFICATION ON MSMT17->DUKEMTMC-REID	2017-11-19
High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks \| Lidan Wang • Vishwanath A. Sindagi • Vishal M. Patel # 2 FACE SKETCH SYNTHESIS ON CUHK	2017-10-27
Face Transfer with Generative Adversarial Network Runze Xu • Zhiming Zhou • Weinan Zhang • Yong Yu	2017-10-17
Learning Transferable Architectures for Scalable Image Recognition \| Barret Zoph • Vijay Vasudevan • Jonathon Shlens • Quoc V. Le # 35 IMAGE CLASSIFICATION ON IMAGENET	2017-07-21
Attribute-Guided Face Generation Using Conditional CycleGAN Yongyi Lu • Yu-Wing Tai • Chi-Keung Tang	2017-05-28
Exploring the structure of a real-time, arbitrary neural artistic stylization network \| Golnaz Ghiasi • Honglak Lee • Manjunath Kudlur • Vincent Dumoulin • Jonathon Shlens	2017-05-18
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks \| Jun-Yan Zhu • Taesung Park • Phillip Isola • Alexei A. Efros # 2 FACIAL EXPRESSION TRANSLATION ON CELEBA	2017-03-30
Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis \| Dmitry Ulyanov • Andrea Vedaldi • Victor Lempitsky	2017-01-09
Instance Normalization: The Missing Ingredient for Fast Stylization \| Dmitry Ulyanov • Andrea Vedaldi • Victor Lempitsky	2016-07-27

Tasks

TASK	PAPERS	SHARE
Image-to-Image Translation	38	15.14%
Domain Adaptation	21	8.37%
Image Generation	19	7.57%
Semantic Segmentation	16	6.37%
Style Transfer	14	5.58%
Voice Conversion	11	4.38%
Object Detection	8	3.19%
Unsupervised Domain Adaptation	6	2.39%
adversarial training	5	1.99%

Usage Over Time

This feature is experimental; we are continuously improving our matching algorithm.

Components

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

Categories

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Normalization