Description: Instance normalization is a technique used in convolutional neural networks (CNNs) that aims to improve the stability and speed of training deep learning models. This technique focuses on normalizing the features of each input instance, meaning each image or sample fed into the network. By doing so, the feature values are adjusted to have a mean of zero and a standard deviation of one, which helps the model learn more efficiently. Instance normalization is particularly useful in situations where the input features may vary significantly between different instances, which could hinder the learning process. This technique differs from other forms of normalization, such as batch normalization, as it is applied to each instance individually, allowing the network to focus on the specific characteristics of each input. In summary, instance normalization is a valuable tool in the data preprocessing arsenal that helps improve convergence and overall performance of convolutional neural networks.
History: Instance normalization was introduced in 2016 by Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky in their paper ‘Instance Normalization for Style Transfer’. This approach was initially developed to enhance style transfer in generative networks, where finer control over the stylistic features of generated images was required. Since then, the technique has been adopted in various deep learning applications, especially in tasks related to computer vision.
Uses: Instance normalization is primarily used in style transfer tasks and in generative adversarial networks (GANs), where maintaining the stylistic coherence of generated images is crucial. It has also been used in image segmentation applications and in image classification models, where variability between instances can affect model performance.
Examples: An example of instance normalization usage can be found in the style transfer model by Ulyanov et al., where it is applied to adjust the features of content and style images. Another example is in GANs, where it is used to enhance the quality of generated images by controlling the characteristics of each input instance.
