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[[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.]] | ||
β | 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]] | + | 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. |