Instance normalization is a technique used in deep learning to normalize the features of each individual training example independently. It adjusts the mean and variance for each instance within a mini-batch, ensuring that the inputs to the neural network layers are on a similar scale. This approach is particularly useful in tasks involving style transfer, where maintaining the characteristics of individual instances is crucial for generating visually appealing results.
congrats on reading the definition of Instance Normalization. now let's actually learn it.