Adaptive Instance Normalization is a normalization method that can help make images look better. When we talk about images, we usually mean pictures, like the ones we take with a camera or download from the internet. But we can also talk about other things that involve images, like videos, games, and virtual reality.
What is Normalization?
Before we talk about Adaptive Instance Normalization, let's first talk about normalization. Normalization is a way to make sure that different pieces of data have the same range of values. This can be important for many reasons. For example, in a machine learning model, we want input data to be normalized so that the model can learn to make accurate predictions. Some normalization techniques include [Batch Normalization](https://paperswithcode.com/method/batch-normalization), [Instance Normalization](https://paperswithcode.com/method/instance-normalization), and [Conditional Instance Normalization](https://paperswithcode.com/method/conditional-instance-normalization).
What is Adaptive Instance Normalization?
Adaptive Instance Normalization (AdaIN) is a type of normalization that can be used for images. It is an extension of Instance Normalization, which normalizes an input to a single style that is specified by affine parameters. Instead of having affine parameters that are learned by the model like in other normalization techniques, AdaIN computes the affine parameters adaptively from a style input. In other words, AdaIN aligns the channel-wise mean and variance of a content input to match those of a style input.
The formula for AdaIN is:
$$ \textrm{AdaIN}(x, y)= \sigma(y)\left(\frac{x-\mu(x)}{\sigma(x)}\right)+\mu(y) $$
Here, $x$ is the content input and $y$ is the style input. $\mu(x)$ is the mean of $x$, $\sigma(x)$ is the standard deviation of $x$, $\mu(y)$ is the mean of $y$, and $\sigma(y)$ is the standard deviation of $y$. By computing the mean and standard deviation of the style input $y$, AdaIN can apply the style to the content input $x$. This can result in images that have a similar style to the style input.
Why is Adaptive Instance Normalization Useful?
Adaptive Instance Normalization is useful because it can help make images look better. For example, if we have a content image and a style image, we can use AdaIN to apply the style of the style image to the content image. This can create a new image that has the content of the content image and the style of the style image. This technique is called style transfer.
Style transfer can be used for many things. For example, we can use it to make our photos look like paintings or to create realistic images in virtual reality. We can also use it to modify existing images or to generate new images.
How is Adaptive Instance Normalization Different from Other Normalization Techniques?
Adaptive Instance Normalization is different from other normalization techniques in a few ways. First, it is a normalization technique that can be used for images. Second, it computes the affine parameters adaptively from a style input instead of having learnable affine parameters like in Batch Normalization or Instance Normalization. Third, it aligns the channel-wise mean and variance of a content input to match those of a style input.
Compared to Batch Normalization, which normalizes the mean and variance of input data per mini-batch, AdaIN normalizes the mean and variance of input data per channel. This means that AdaIN can be used for style transfer, while Batch Normalization cannot.
Compared to Instance Normalization, which normalizes the mean and variance of input data per instance (i.e., per image or per feature map), AdaIN normalizes the mean and variance of input data per channel. This means that AdaIN can be used for style transfer across channels, while Instance Normalization cannot.
Examples of Adaptive Instance Normalization
There are many examples of Adaptive Instance Normalization in practice. One popular use case is style transfer, where the style of one image is transferred to another image. Another use case is image generation, where new images are generated using random noise inputs and AdaIN. Here are a few examples of Adaptive Instance Normalization in action:
Style Transfer:
In this example, the style of Vincent Van Gogh's "The Starry Night" is transferred to a photo of a city at night. The style of the painting is apparent in the new image, but the content of the photo is still recognizable.
Image Generation:
In this example, new images of bedrooms are generated using random noise inputs and AdaIN. The images have a similar style to the input style image, but the content of the bedrooms is different in each image.
Adaptive Instance Normalization is a normalization technique that can be used for images. It aligns the mean and variance of the content features with those of the style features by computing the affine parameters adaptively from a style input. AdaIN is different from other normalization techniques in that it normalizes the mean and variance of input data per channel and aligns the channel-wise mean and variance of a content input to match those of a style input. Adaptive Instance Normalization can be used for style transfer and image generation, among other things.
