Instance Normalization

Instance Normalization is a technique used in neural networks to improve their ability to generate consistent results when processing images. It works by adjusting the pixel values within each individual image (or instance) so that their overall brightness and contrast are standardized. This helps the network focus on the underlying features rather than being affected by variations in lighting or style. Essentially, it normalizes each image separately, making the network more stable and effective in tasks like style transfer, where maintaining consistent results across different images is important.