Editing 2671: Rotation

Jump to: navigation, search

Warning: You are not logged in. Your IP address will be publicly visible if you make any edits. If you log in or create an account, your edits will be attributed to your username, along with other benefits.

The edit can be undone. Please check the comparison below to verify that this is what you want to do, and then save the changes below to finish undoing the edit.
Latest revision Your text
Line 12: Line 12:
 
[[Image:World lines and world sheet.svg|thumb|200px|{{w|String theory}} describes the {{w|worldline}}s of point-like particles as {{w|worldsheet}}s of "closed strings," forming a topological  foam.]]
 
[[Image:World lines and world sheet.svg|thumb|200px|{{w|String theory}} describes the {{w|worldline}}s of point-like particles as {{w|worldsheet}}s of "closed strings," forming a topological  foam.]]
  
βˆ’
For a fuller explanation of the concepts involved, including {{w|Planck units}}, often associated with the topological {{w|quantum foam}} of {{w|string theory}}, please see [https://www.youtube.com/watch?v=pUF5esTscZI this CGP Grey video.] For an explanation of topological string theory, see [[2658: Coffee Cup Holes]]. Please see also [[1683: Digital Data]] for an analogous image processing concept.
+
For a fuller explanation of the concepts involved, including {{w|Planck units}}, often associated with the topological {{w|quantum foam}} of {{w|string theory}}, please see [https://www.youtube.com/watch?v=pUF5esTscZI this CGP Grey video.] For an explanation of topological string theory, see [[2658: Coffee Cup Holes]].
  
 
The title text refers to producing photographically likely higher resolution images from lower resolutions, an active area of current research.[https://openaccess.thecvf.com/content/ICCV2021/papers/Liang_Hierarchical_Conditional_Flow_A_Unified_Framework_for_Image_Super-Resolution_and_ICCV_2021_paper.pdf] Because reducing the resolution of an image is a lossy process, results obtained through such processes will not be able to perfectly recreate the original. Machine learning can be used to calculate how images of known photographic subjects (or e.g. anime-style art, in the case of {{w|waifu2x}}) behave under certain types of noise or reduction in size, so that images ''of those kinds'' can be upscaled in a way that, if not perfectly recreating the original, at least is a faithful representation, but when the image is scaled all the way down to one pixel, everything except a small amount of data about the image's overall color is lost, making reconstructing the original image impossible. Randall disclaims that, because the AI upscaling is based on ingesting a large corpus of human-made art (with subjects that we find 'interesting' or at least meaningful being predominantly represented), the AI will produce an image that is at least as cool as the original image was, and in fact some image generation AIs actually work on a similar principle β€” for example, "reverse diffusion" AIs are trained by teaching them to reconstruct images from noise, at which they can produce entirely new images by being fed ''actual'' noise.  He could also be making a pun on {{w|color temperature}}, which the upscaler will be able to match to the original image. The "{{tvtropes|EnhanceButton|enhance button}}" for upscaling images is a common trope in movies and television, especially in crime and science fiction stories.
 
The title text refers to producing photographically likely higher resolution images from lower resolutions, an active area of current research.[https://openaccess.thecvf.com/content/ICCV2021/papers/Liang_Hierarchical_Conditional_Flow_A_Unified_Framework_for_Image_Super-Resolution_and_ICCV_2021_paper.pdf] Because reducing the resolution of an image is a lossy process, results obtained through such processes will not be able to perfectly recreate the original. Machine learning can be used to calculate how images of known photographic subjects (or e.g. anime-style art, in the case of {{w|waifu2x}}) behave under certain types of noise or reduction in size, so that images ''of those kinds'' can be upscaled in a way that, if not perfectly recreating the original, at least is a faithful representation, but when the image is scaled all the way down to one pixel, everything except a small amount of data about the image's overall color is lost, making reconstructing the original image impossible. Randall disclaims that, because the AI upscaling is based on ingesting a large corpus of human-made art (with subjects that we find 'interesting' or at least meaningful being predominantly represented), the AI will produce an image that is at least as cool as the original image was, and in fact some image generation AIs actually work on a similar principle β€” for example, "reverse diffusion" AIs are trained by teaching them to reconstruct images from noise, at which they can produce entirely new images by being fed ''actual'' noise.  He could also be making a pun on {{w|color temperature}}, which the upscaler will be able to match to the original image. The "{{tvtropes|EnhanceButton|enhance button}}" for upscaling images is a common trope in movies and television, especially in crime and science fiction stories.

Please note that all contributions to explain xkcd may be edited, altered, or removed by other contributors. If you do not want your writing to be edited mercilessly, then do not submit it here.
You are also promising us that you wrote this yourself, or copied it from a public domain or similar free resource (see explain xkcd:Copyrights for details). Do not submit copyrighted work without permission!

To protect the wiki against automated edit spam, we kindly ask you to solve the following CAPTCHA:

Cancel | Editing help (opens in new window)