
Image Completion with Deep Learning in TensorFlow - semanser
http://bamos.github.io/2016/08/09/deep-completion/
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michael_h
Oh man, those facial images are deep in the uncanny valley. Maybe on the
rising side of the slope now, but still way down there.

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soldeace
Have you seen the animation at the end of the article, where progressively
better completions are selected? Most of them seemed pretty natural to me.

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michael_h
Yeah, I saw them - a little difficult to tell with the loop, but they still
seem 'off'. Buh.

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bdamos
Thanks for the feedback, I just updated the animation to pause at the
beginning and end to make the completions clearer:
[https://github.com/bamos/bamos.github.io/commit/f6a152851355...](https://github.com/bamos/bamos.github.io/commit/f6a1528513555962e3f2da0ad7e15fde4604baeb)

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sforzando
I was impressed with the Labeled Faces in the Wild (LFW) facial auto-
completion results, especially since the system was not trained on LFW at all!
The results seemed almost too good to be true. Perhaps this is a testament
that there isn't _that_ much diversity in human faces?

Very well written overall, and I appreciated the author's thoughts on
TensorFlow+torch at the end of the article.

Adversarial training is a fascinating idea, and I love the sound of it. I'd
like to start applying that concept in the future.

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canada_dry
On a similar vein...

[http://www.pyimagesearch.com/2016/08/10/imagenet-
classificat...](http://www.pyimagesearch.com/2016/08/10/imagenet-
classification-with-python-and-keras/)

Uses TF deep learning to classify an image.

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jackcosgrove
It would be cool if you could use a GAN to generate images merely by providing
an object name. You could train the GAN on images obtained by searching for
that object name in an image search engine.

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deegles
Could this be used to generate unique images from a training set?

