title: Instance Normalisation vs Batch normalisation
body: <p>I understand that Batch Normalisation helps in faster training by turning the activation towards unit Gaussian distribution and thus tackling vanishing gradients problem. Batch norm acts is applied differently at training(use mean/var from each batch) and test time (use finalized running mean/var from training phase).</p>
  
  <p>Instance normalisation, on the other hand, acts as contrast normalisation as mentioned in this paper <a href="https://arxiv.org/abs/1607.08022" rel="noreferrer">https://arxiv.org/abs/1607.08022</a> . The authors mention that the output stylised images should be not depend on the contrast of the input content image and hence Instance normalisation helps. </p>
  
  <p>But then should we not also use instance normalisation for image classification where class label should not depend on the contrast of input image. I have not seen any paper using instance normalisation in-place of batch normalisation for classification. What is the reason for that? Also, can and should batch and instance normalisation be used together. I am eager to get an intuitive as well as theoretical understanding of when to use which normalisation. </p>