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According to the information from the site: We have built a proprietary dataset by taking tens of thousands of images of people in our studio. These photos are taken in a controlled environment allowing us to make sure that each face has consistent look and quality. After shooting, photos are tagged, categorized, and added to a dataset that is used for ...


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Yes, it is a bit misleading. What it really means is input channels, so it would be: nn.Conv2d: Applies a 2D convolution over an input signal composed of several input channels. So, why don't just use channels instead of input planes? Well, initially the major deep learning applications were used for computer vision or image processing approaches. In CV or ...


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Depth maps are created using principles of photometry (method of measuring light). The depth maps (rather images) you took from the website are "images" not exact depth "maps". So by default when you pull out a png image from a webpage, it will be saved in "RGB". That is the reason you got an array with 3 layers. In practice, it ...


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You were correct in that the model won't be able to reconstruct any missing information with complete certainty (an intuitively impossible task). As Oliver Mason mentioned, it is estimating what a similar image (from the training data it has been exposed to) would have looked like (I should note that in the vast majority of cases, we don't/can't actually ...


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You actually don't have to loose information if you don't fulfill Nyquist — although that topic is quite advanced and has limitations. Still, super resolution is reliable and used by most 4K TVs today to upscale 1080p video to fit the 4K screen. You may notice TV ads for 4K TVs occasionally mentioning this. What super resolution does is just generalising ...


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Yes, it's guessing. In the training phase, you show it lots of coarse and detailed pictures, and the algorithm learns a mapping from course to detailed. Then you present it a new coarse image, and it executes the same mapping. The information from the original picture is gone, and it cannot be retrieved, so it's filled in by analogy to other cases. "...


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