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> The resulting images look nice but there's no guarantee that all the extra detail visible in those images is genuine detail and not just "believable" data fil

> The resulting images look nice but there's no guarantee that all the extra detail visible in those images is genuine detail and not just "believable" data filled in by the net. Maybe fine for happy snaps but it's very important that users of the camera know that its output is just an "artist's impression." I completely agree with this, and it's more and more dangerous as the resulting images appear more and more realistic. On a related tangent, this also showed up recently: http://fortune.com/2018/04/24/nvidia-artificial-intelligence-images/ http://fortune.com/2018/04/24/nvidia-artificial-intelligence... A lot of people I know -- intelligent people who are familiar with machine learning and image manipulation -- were confused as to how this approach was "recovering" data. It's not recovering data at all; it's guessing and filling in blanks, but doing so in such a realistic fashion that apparently it's poking around in some blind spots because the result is so convincing that you think it's the "real" image. I feel like the same blind spot would be attacked with "seeing in the dark" as well.

Gravityloss 8y ago
It's the attention economy where you compete for the attention and thus funding by showing pictures.
feintruled 8y ago
Reminds me of the old joke, back when people had to manually touch up photos. Man goes to the photographer with an old family photo. Says, "This is the only photo we have of Grandfather, but I don't like that he's wearing a hat. Can you remove it?" The photographer says, "Sure, what sort of hairstyle did he have?" And the guy says, "Won't you find out when you take off the hat?"
dahart 8y ago
> On a related tangent, this also showed up recently: (NVIDIA’s inpainting). A lot of people I know -- intelligent people who are familiar with machine learning and image manipulation -- were confused as to how this approach was "recovering" data. Realistic image inpainting & synthesis has been going on for decades, so I’d guess the main confusion is due to reading the title of Fortune’s article, rather than the paper’s title “Image Inpainting for Irregular Holes Using Partial Convolutions”. BTW, Kudos to Fortune for actually linking to the paper. I felt like it was pretty obviously inpainting, and suggesting arbitrary training data just from watching the video, so maybe the confusion was from reading the PR title only, and not diving any deeper? Here’s my favorite inpainting paper, partly because the author is a friend, but also because it’s able to hallucinate written text, which most inpainting algorithms since then haven’t been able to do. It’s not a neural network though, and the training data comes from the single input image itself. http://graphics.cs.cmu.edu/people/efros/research/EfrosLeung.html http://graphics.cs.cmu.edu/people/efros/research/EfrosLeung.... > it’s more and more dangerous... I feel like the same blind spot would be attacked with “seeing in the dark” as well. It’s possible, yes, but it does depend on what the authors did, how the network was trained, whether they allow reconstruction from pure noise, etc.. I would agree that this paper title is a bit provocative, and suggests assuming the output is realistic. The problem might be the title, and not the technique. While it is important to understand that NNs are hallucinating output with training data, it’s also a good idea to reflect on the history of analog & digital photography & photoshop, and recall that this slippery slope of danger against fake realism has been warned against multiple times before. There are lots of legitimate uses for inpainting (movies, ads). As someone who’s worked in film, I’m excited about the possibilities that NNs bring in terms of new techniques and reduction of labor.
- sandworm101 8y ago
  >> I’m excited about the possibilities that NNs bring in terms of new techniques and reduction of labor. Reduction of labour is nice. Elimination of labour is problematic. To photoshop something someone has to actually photoshop it. We don't set our cameras to automatically photoshop things as we take them. The techniques in the OP are dangerous because they could be employed in situations where the photographer doesn't realize. A camera/robot trying to edit something automatically fabricating a false reality in the eyes of humans. Imagine a camera used to capture evidence of crime. You don't want such a thing filling in details on its own.
  - TeMPOraL 8y ago
    Isn't Google Photos / Google Camera App doing that, automatically or semi-automatically? I recall they had a feature that could magically merge multiple photos into one that has a combination of details from the source images.
amelius 8y ago
> On a related tangent, this also showed up recently: ... Interesting link. But funny that when in the face of a person one eye is masked out, the algorithm doesn't match the hallucinated eye with the unmasked eye. I'd hope that a smart ML network would understand the constraint that both eyes should look the same.

Gravityloss 8y ago

It's the attention economy where you compete for the attention and thus funding by showing pictures.

feintruled 8y ago

Reminds me of the old joke, back when people had to manually touch up photos. Man goes to the photographer with an old family photo. Says, "This is the only photo we have of Grandfather, but I don't like that he's wearing a hat. Can you remove it?" The photographer says, "Sure, what sort of hairstyle did he have?" And the guy says, "Won't you find out when you take off the hat?"

dahart 8y ago

> On a related tangent, this also showed up recently: (NVIDIA’s inpainting). A lot of people I know -- intelligent people who are familiar with machine learning and image manipulation -- were confused as to how this approach was "recovering" data. Realistic image inpainting & synthesis has been going on for decades, so I’d guess the main confusion is due to reading the title of Fortune’s article, rather than the paper’s title “Image Inpainting for Irregular Holes Using Partial Convolutions”. BTW, Kudos to Fortune for actually linking to the paper. I felt like it was pretty obviously inpainting, and suggesting arbitrary training data just from watching the video, so maybe the confusion was from reading the PR title only, and not diving any deeper? Here’s my favorite inpainting paper, partly because the author is a friend, but also because it’s able to hallucinate written text, which most inpainting algorithms since then haven’t been able to do. It’s not a neural network though, and the training data comes from the single input image itself. http://graphics.cs.cmu.edu/people/efros/research/EfrosLeung.html http://graphics.cs.cmu.edu/people/efros/research/EfrosLeung.... > it’s more and more dangerous... I feel like the same blind spot would be attacked with “seeing in the dark” as well. It’s possible, yes, but it does depend on what the authors did, how the network was trained, whether they allow reconstruction from pure noise, etc.. I would agree that this paper title is a bit provocative, and suggests assuming the output is realistic. The problem might be the title, and not the technique. While it is important to understand that NNs are hallucinating output with training data, it’s also a good idea to reflect on the history of analog & digital photography & photoshop, and recall that this slippery slope of danger against fake realism has been warned against multiple times before. There are lots of legitimate uses for inpainting (movies, ads). As someone who’s worked in film, I’m excited about the possibilities that NNs bring in terms of new techniques and reduction of labor.

sandworm101 8y ago
>> I’m excited about the possibilities that NNs bring in terms of new techniques and reduction of labor. Reduction of labour is nice. Elimination of labour is problematic. To photoshop something someone has to actually photoshop it. We don't set our cameras to automatically photoshop things as we take them. The techniques in the OP are dangerous because they could be employed in situations where the photographer doesn't realize. A camera/robot trying to edit something automatically fabricating a false reality in the eyes of humans. Imagine a camera used to capture evidence of crime. You don't want such a thing filling in details on its own.
- TeMPOraL 8y ago
  Isn't Google Photos / Google Camera App doing that, automatically or semi-automatically? I recall they had a feature that could magically merge multiple photos into one that has a combination of details from the source images.

amelius 8y ago

> On a related tangent, this also showed up recently: ... Interesting link. But funny that when in the face of a person one eye is masked out, the algorithm doesn't match the hallucinated eye with the unmasked eye. I'd hope that a smart ML network would understand the constraint that both eyes should look the same.