Instance normalization is a technique used to normalize the features of individual training examples in neural networks, aiming to stabilize and accelerate the training process. This method works by standardizing the mean and variance of each feature for every instance independently, which helps to mitigate the internal covariate shift and allows for improved performance in tasks like style transfer and image generation.
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