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Earlier quoted context omitted.
The title needs to be changed so brains recognize it as such. It either needs a preceding adjective or letter indicating what type of convolutional network it is. The other option is spelling it out. Most people will read CNN as the news channel. Even those familiar with neural networks.
yeah, i read it exactly as CNN == Cable News Network and was confused for while...
Impressive of the American news channel, CNN, to convert images in minus one second.
I understand that the title may have brought confusion, and I think your comment calls attention to this whilst also being somewhat funny. But still, could we make an effort not to devolve into what has happened on Reddit, i.e. comment sections which mainly consist of puns and other low effort jokes?
Earlier quoted context omitted.
I understand that the title may have brought confusion, and I think your comment calls attention to this whilst also being somewhat funny. But still, could we make an effort not to devolve into what has happened on Reddit, i.e. comment sections which mainly consist of puns and other low effort jokes?
I don't understand what you are saying, it is very "HN" to comment on the title and not on the article. And it is even more "HN" to comment on details of the title or the article because you don't really know what to say about the article. Look at my comment.
It's a fight worth fighting. "C'mon it's just a joke lol" or "it's only a comment about the title" only assist in that transformation.
The problem with techniques like this is that they fundamentally amount to ‘making a plausible guess as to what the image would look like’, since essentially they can’t extract information that is simply not there. There is a Shannon entropy limit here. Machine learning is really machine-enhanced educated-guesswork, which has its place but also has its limits.
Counterpoint: The human brain converting a 2D image to a 3D model is educated-guesswork too :)
As a photographer, the comparison to "raw" results without color balance or noise removal seems somewhat deceptive. The effects visible in the video seem easy to quickly replicate with existing techniques, such as the "surface blur" filter that averages out pixel values in areas with similar color. This happens at the expense of detail in low-contrast areas, producing a plastic-like appearance of human skin and hair,…
Interestingly, this effect is notably visible in their example image [0]. Notice the distinctly "plasticized" appearance of the book cover, and how the text is not intelligible in the low-contrast areas of the reflection. [0]: https://raw.githubusercontent.com/cchen156/Learning-to-See-i...
- Did they create a special network topology for this problem?
- Does the network need to see the entire image, or only an NxN subblock at a time?
- How did they obtain the training data? Is it possible to take daylight images and automatically turn them into nighttime images somehow?