6 ms·

Colorful Image Colorization

54 points by alexcasalboni 11y ago

filleokus 11y ago
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
- acqq 11y ago
  The best link with a lot of examples is on the top of previous discussion, by saurik: http://richzhang.github.io/colorization/resources/imagenet_comparison.html http://richzhang.github.io/colorization/resources/imagenet_c...
Matumio 11y ago
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.
- CardenB 11y ago
  I wonder if stylenet would be better suited for this
murbard2 11y ago
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.
- kordless 11y ago
  It's all game theory, at the low levels.
dkopi 11y ago
"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.
narengowda 11y ago
Feed forward == machine learning ??
- zardo 11y ago
  Yes, a feed forward neural network is a subset of machine learning.
jcoffland 11y ago
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.
WhitneyLand 11y ago
It's interesting work, but what are the the applications?

54 points by alexcasalboni 11y ago

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

acqq 11y ago
The best link with a lot of examples is on the top of previous discussion, by saurik: http://richzhang.github.io/colorization/resources/imagenet_comparison.html http://richzhang.github.io/colorization/resources/imagenet_c...

Matumio 11y ago

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.

CardenB 11y ago
I wonder if stylenet would be better suited for this

murbard2 11y ago

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.

kordless 11y ago
It's all game theory, at the low levels.

dkopi 11y ago

"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.

narengowda 11y ago

Feed forward == machine learning ??

zardo 11y ago
Yes, a feed forward neural network is a subset of machine learning.

jcoffland 11y ago

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.

WhitneyLand 11y ago

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