What's the difference if CycleGAN uses instance normalization or batch normalization?

In CycleGAN, instance normalization and batch normalization are two different techniques used to normalize the activations of a layer. The main difference between instance normalization and batch normalization is how the normalization is performed.

Batch normalization applies normalization across a batch of samples, which can lead to some loss of information across samples. Instance normalization, on the other hand, applies normalization across the channels of a single sample, preserving information within the sample.

If CycleGAN uses instance normalization instead of batch normalization, it can better preserve the style of individual images and make the model less sensitive to batch size. This can result in better quality image-to-image translations and reduce the likelihood of artifacts or blurring in the generated images.

However, the choice between instance normalization and batch normalization ultimately depends on the specific problem being solved and the characteristics of the data. In some cases, batch normalization may work better than instance normalization, and vice versa.