Overview of Conditional Instance Normalization
Conditional Instance Normalization is a technique used in style transfer networks to transform a layer’s activations into a normalized activation specific to a particular painting style. This normalization approach is an extension of the instance normalization technique.
What is instance normalization?
Before diving into Conditional Instance Normalization, it’s important to understand instance normalization. Instance normalization is a method of normalizing the activations of a convolutional neural network (CNN) by subtracting the mean and dividing by the standard deviation of each individual feature map separately. This technique helps in reducing the impact of changes in brightness and contrast in the input image.
What is Conditional Instance Normalization?
Conditional Instance Normalization (CIN) is a modification of instance normalization that allows shared convolutional weights across many styles. This technique transforms a layer’s activations $x$ into a normalized activation $z$ specific to painting style $s$. Instead of using the same normalization parameters for all styles, CIN augments the $\gamma$ and $\beta$ parameters so that they are $N \times C$ matrices, where $N$ is the number of styles being modeled and $C$ is the number of output feature maps.
The normalization is achieved through the following equation:
$$ z = \gamma\_{s}\left(\frac{x - \mu}{\sigma}\right) + \beta\_{s}$$
Here, $\mu$ and $\sigma$ are computed using the mean and standard deviation of the input feature maps. $\gamma\_{s}$ and $\beta\_{s}$ are obtained by selecting the row corresponding to $s$ in the $\gamma$ and $\beta$ matrices. The conditional aspect of CIN is that the $\gamma$ and $\beta$ parameters are conditioned on the specific painting style.
Benefits of Conditional Instance Normalization
CIN has several benefits, one of which is the ability to stylize a single image into $N$ painting styles with a single feedforward pass of the network. Another benefit is that it allows for shared weights across many styles, which can help to reduce the size of the model and make training more efficient.
Applications of Conditional Instance Normalization
Conditional Instance Normalization has shown promising results in various applications such as image stylization, super-resolution, and semantic segmentation. In image stylization, CIN can be used to transform an input image into a desired painting style. In super-resolution, CIN can be used to enhance the details of an input image while preserving its original style. In semantic segmentation, CIN can be used to segment an image into different object categories while maintaining the consistency of the style across the image.
Conditional Instance Normalization is a powerful technique for stylizing images and has shown promising results in various applications. It allows for shared weights across many styles, which makes training more efficient and reduces the size of the model. With its ability to stylize a single image into multiple painting styles with a single pass, it offers a unique solution to a common problem in image stylization.
