1.Batch Normalization:根据batch,对batch做归一化 2.Instance Normalization:channel层面上做归一化 3.Conditional Instance Normalization:对每个风格建一个Instance Normalization。从公式中可以看出每增加一个风格,多了一个gama^s和beta^s. 所以一个网络需要为其多增加2F
CIN成功背后的原因也很清楚:不同的定义参数可以将特征统计归一化为不同的值,从而将输出图像归一化为不同风格。 5、Adaptive Instance Normalization 如果IN将输入标准化为由定义参数指定的单个样式,是否可以通过使用自适应定义转换将其调整为任意给定的样式?在这里,我们提出了一个对IN的简单扩展,我们称之为自适应实例...
Adaptive Instance Normalization (AdaIN) 接收一张内容图 X 和一张风格图 Y,不引入参数,自动计算出其均值和方差,只需使用一次前向网络,即可完成风格网络的生成,将大大提高执行速度。与 BN,IN,CIN 不同,AdaIN 没有可学习的 affine parameters 。其根据输入的 style image,自适应的生成 affine parameters。 损失函...
综合了Instance Normalization和Layer Normalization的优点,通过可学习的系数ρ调整两种归一化的权重。
2. Interpreting Instance Normalization: 略 3. Adaptive Instance Normalization: 既然IN 可以根据 affine parameters 将输入归一化为 single style,那么,有没有可能,我们给定多种自适应的 affine transformations 来生成任意给定类型的图像呢?基于该动机,作者对 IN 的技术进行了拓展,提出了 Adaptive Instance Normalizatio...
This repository contains the code (inTorch) for the paper: Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization Xun Huang,Serge Belongie ICCV 2017 (Oral) This paper proposes the first real-time style transfer algorithm that can transferarbitrarynew styles, in contrast toa singl...
To tackle the problem, we propose a novel framework called Progressive Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization (PRO-U-GAT-IT) for the unsupervised image-to-image translation task. In contrast to existing attention-based models that fail to handle ...
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization Xun Huang Serge Belongie Department of Computer Science & Cornell Tech, Cornell University {xh258,sjb344}@cornell.edu Abstract Gatys et al. recently introduced a neural algorithm that renders a content image in the style of ...
Region-aware Adaptive Instance Normalization for Image Harmonization Supplementary Material Jun Ling1, Han Xue1, Li Song1,2 , Rong Xie1, Xiao Gu1 1Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, China 2MOE Key Lab of Artificial Intelligence, AI ...
同时受启发于instance normalization(IN),并且本文作者对于其做了解释之后启发了这篇工作,也就是说IN比BN要更关注风格信息,作者对于IN做了一个简单的扩展,称为adaptive instance normalization (AdaIN)。给定一个内容输入和样式输入特征图,AdaIN 简单地调整内容的输入的均值和方差以匹配(也可以称为拟合或者对齐)样式输...