Neural style transfer allows computers to take an image and apply the visual style of another image to generate a new image. This is done by defining content and style cost functions that measure how well the generated image matches the content of the input image and the style of the style image. The algorithm then uses gradient descent to minimize the total cost and iteratively update the generated image until the content and style are closely matched.

1. Neural Style Transfer
How computers dream
By
Keshan Sodimana

2. What is so cool about
Prisma?

3. Content
+
Style
Generated

5. Content Loss / Content Cost function

6. Style Loss / Style Cost Function

9. Gram Matrix
● The Gram matrix is a square matrix that contains the dot
products between each vectorized filter in layer l.
● The Gram matrix can therefore be thought of as a non-
normalized correlation matrix for filters in layer l.

10. Content loss function
Gram matrix
Style loss for a given
layer
Style loss

11. Finally we need to minimize this total
loss..

12. How to generate the image:
1. Initiate the generated image with random noise
2. Calculate content and style costs
3. Use gradient descent to minimize cost of generated image or
the total loss as seen in the previous slide.

14. Enough bullshit let’s code!