While training (Variational)-Autoencoder networks, I came along the paper by Higgins et al. "DARLA" where she stated:
The shortcomings of calculating the log-likelihood term [...] on a per-pixel basis are known and have been addressed in the past by calculating the reconstruction cost in an abstract, high-level feature space given by another neural network model, such as a GAN [..] or a pre-trained AlexNet [...]
Since there is no statement regarding why it is better to use feature-space instead of pixel-space, I am wondering why the results become better in the first case. I assume that since the per-pixel loss encourages the whole image to be trained correctly, a feature based loss can only fetch the perceptual similarity. Therefore, the per-pxel loss should always result in better outputs, or is there any important influence by the regularizer which I am missing?