
Transfer Style but Not Color - ot
http://blog.deepart.io/2016/06/04/color-independent-style-transfer/
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Everlag
I played with style transfer on a bunch of paintings from the Wikipedia
featured paintings[0]. The result when it works well is absolutely fantastic
but when it doesn't, it basically just applies noise. Even 'obnoxious' styles
like Van Gogh need to be cherry picked for the most 'featureful' flavor
images.

That being said, I am pretty hyped to get my 1080 to be able process more than
1 image an hour at 720p. Also, take a look at the featured paintings, they're
all public domain and absolutely gorgeous.

[0]
[https://en.wikipedia.org/wiki/Wikipedia:Featured_pictures/Ar...](https://en.wikipedia.org/wiki/Wikipedia:Featured_pictures/Artwork/Paintings)

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sabalaba
I just re-implemented something similar for Dreamscope that gets very similar
results. If you want to do it yourself, simply convert the original and style
image to HSV and create a third image like this:

    
    
        [H, S, V] = RGB2HSV(original)
        [H_s, S_s, V_s] = RGB2HSV(style)
        result = HSV2RGB([H, S, V_s])
    

You can play with the algorithm on Dreamscope, just click the "Original Color"
star before processing!

[https://dreamscopeapp.com](https://dreamscopeapp.com)

~~~
a_ecker
Dude, you're fast :)

Our method is a little bit more involved than that, though. We're preparing a
short writeup.

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j2kun
Is there a github repository for this variant? Or a paper?

[Edit: found an attempt here: [https://github.com/pavelgonchar/color-
independent-style-tran...](https://github.com/pavelgonchar/color-independent-
style-transfer)]

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amelius
Can we do similar things with music?

~~~
versteegen
This works for images because images can be decomposed into different scales:
in classical image processing into image pyramids, and in this case using deep
convolutional neural networks which capture progressively more complex
features and larger scales. So you decompose an image, keeping the high level
features fixed, and modify the image (using gradient ascent) to make the low
level features match those from a sample of the artistic style.

So to use the same algorithm for music you would have to decompose audio in a
similar meaningful way. There has also been a lot of success in speech
recognition with CNNs lately, but I don't know what the situation is with
modelling music.

~~~
amelius
> So to use the same algorithm for music you would have to decompose audio in
> a similar meaningful way.

Well, in music, scaling could be compared to increasing/decreasing frequency.
We all know that a song which is transposed by e.g. an octave still sounds the
same (albeit lower/higher). So I think the concept translates well from
images.

~~~
versteegen
That doesn't sound like what I meant. The point of image pyramids is that they
separate fine details (e.g. style) from coarse details (form). A note
transposed down an octave is still exactly the same type of object; it's not
more abstract so frequency is not an analog to scale in images.

What you want is a progression of abstractions, e.g. note, chord, melody...
but one where 1) each is largely orthogonal so that they can be separated and
recombined with a lot of flexibility (definitely not true of notes and
melodies) 2) it be computed straightforwardly, preferably by a differentiable
function 3) can separate style and form.

