

Show HN: Deep Learning Shirt for Charity - some1else
http://some1else.tumblr.com/post/124922361439/deep-learning-shirt-for-charity

======
therobot24
$30 for a shirt with a screen printed image from some random deep learning
paper? You don't even cite the paper. Assuming the source is published in some
venue then the publisher (ACM, IEEE, etc.) owns the copyright of that image
and while i haven't heard of IEEE going after people for copyright
infringements this seems to be pretty cut and dry.

Also, using the language, "for Charity", doesn't sound great when you link to
a tumblr blog instead of the actual charity website.

The whole thing just looks shady as hell.

~~~
some1else
The price is steep because charity. I used output from Caffe[1] which is open
source software. You can get a similar output yourself, just by running their
Python notebook[2]. The festival website is linked within the post[3]. I am
using Tumblr to see if it's a viable medium for mini fundraisers.

Thanks for the feedback though, I will set up a landing page instead.

[1]: [http://caffe.berkeleyvision.org/](http://caffe.berkeleyvision.org/)

[2]:
[http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/ex...](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb)

[3]: [http://kudplac.si/portfolio/tknp/](http://kudplac.si/portfolio/tknp/)

~~~
therobot24
Looks like the filters from the notebook are a re-organized view of figure 3
from
[http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf](http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf)
(as the caffe page cites at the top). I'm not a lawyer, so i don't really know
if making a 'new' image using the components of the copyrighted image is
derivative or not.

> Thanks for the feedback though, I will set up a landing page instead.

Please do, if this is really legit, it's in your best interest to set it up in
the most professional way possible. It'd probably be better to get the
organizers of the festival to put the content of the post in one of their
pages and you link there.

~~~
some1else
The first layer only contains basic features, which are shared among different
sets of images[1]. It seems that the ImageNet photos themselves are indeed
very protected[2], so I should probably use another dataset as input. Plugging
in my iPhone camera roll should result in very similar filters. It will be
very interesting to see how much they differ.

Update: The author of Caffe just got back to me with word that the filters are
in public domain, therefore safe to use as design elements.

[1]: [http://i.stack.imgur.com/Hl2H6.png](http://i.stack.imgur.com/Hl2H6.png)

[2]:
[http://www.princeton.edu/main/administration/legal_complianc...](http://www.princeton.edu/main/administration/legal_compliance/copyright/)

