a convolutional network architecture that do not have skip connections (e.g. VGG–19). The choice for this representation originates in the observation that higher layers (i.e. closer to the fully connected and to the classification layers) will focus on high-level features, thus onto content (the focus is on objects, not on the actual pixels), while the first layers can be used to reconstruct the image with accurate pixel values.

Differently with respect to previous works which adjust the subject (content) image, the original neural style algorithm [@Gatys:2015] starts from a white noise image, $\textbf{X}$, which is modified in order to match the over–imposed statistics.

Starting from the white noise image, the method employs gradient descent to smoothly adjust values in order to match the content and style of the desired source images. The global loss function is a linear combination between two other loss functions used for content and, respectively, the style matching. In the neural style transfer algorithm, the subject image will provide the content matching, $\mathbf{S}$, while the generated image is $\mathbf{X}$. Considering the activations $F_{ip}^l$ ($P_{ip}^l$) of the $i^{th}$ filter on the $l^{th}$ layer at position $p$ in a CNN representation of $\mathbf{S}$ (and respectively $\mathbf{X}$), the loss 