
Colorful Image Colorization - alexcasalboni
http://arxiv.org/abs/1603.08511
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murbard2
From the abstract: "We evaluate our algorithm using a “colorization Turing
test”, asking human subjects to choose between a generated and ground truth
color image."

And later in the article: "However, the results from these and other past
attempts tend to look desaturated. One explanation is that [1,2] use loss
functions that encourage conservative predictions. [...] We instead utilize a
loss tailored to the colorization problem. As pointed out by [3], color
prediction is inherently multimodal – many objects, such as a shirt, can
plausibly be colored one of several distinct values."

This suggests that adversarial training might be a good fit. It transforms the
goal from "reproduce the original colors", which is in general impossible, to
"produce as convincing a colorization as possible", which is the real goal.

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kordless
It's all game theory, at the low levels.

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filleokus
Demonstration of results:
[http://richzhang.github.io/colorization/#vgg_res](http://richzhang.github.io/colorization/#vgg_res),
EDIT: Also, previous discussion:
[https://news.ycombinator.com/item?id=11403653](https://news.ycombinator.com/item?id=11403653)

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acqq
The best link with a lot of examples is on the top of previous discussion, by
saurik:

[http://richzhang.github.io/colorization/resources/imagenet_c...](http://richzhang.github.io/colorization/resources/imagenet_comparison.html)

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dkopi
"Our method successfully fools humans 20\% of the time, significantly higher
than previous methods."

I suspect this number will only increase over time, as humans lose the ability
to recognize original colors in images thanks to Instagram and photo filters.

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Matumio
Maybe this could evolve into an artist's tool for digital painting, where you
often do colorization as the final step in the workflow. As for adding back
color to photos, I don't see much need for that today, except as a machine
learning benchmark.

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CardenB
I wonder if stylenet would be better suited for this

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jcoffland
The examples in the PDF are impressive. I especially liked the colorized Ansel
Adams photos. Those colorizations are quite good but seeing them in color only
highlights how much more powerful they are in B&W.

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WhitneyLand
It's interesting work, but what are the the applications?

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narengowda
Feed forward == machine learning ??

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zardo
Yes, a feed forward neural network is a subset of machine learning.

