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Under US copyright law, this is probably fair use ...but beware of memorization. You may run into more trouble if the AI outputs things very similar to the original work. Also, consult a lawyer to help you apply the law to your specific situation. This is just information on general legal principles, not any specific situation, and also I'm not a lawyer. ...


4

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. "...


3

Disclaimer that every attorney will give unless formally engaged: This does not constitute formal legal advice. This data was published with the expectation of public view Viewing that data is a form of utilization—taking in information which may be used to make a decision, or just as divertment (here consumption of entertainment media by humans.) If any ...


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One answer is infinite amount of time because it can always be better. Another answer is: 10k for training set A PC with a GPU (3~4k USD), google colab (10 USD per month), or other cloud service (probably more expensive than colab) One developer, 1 day lol Two kinds is easier than multiple kinds There is no paper that seeks to answer your question the way ...


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To verify the accuracy of the classification stage, you will need labeled images with a single car. To train and verify accuracy of the detection stage and full system, you can: in the datasets with images with multiple cars, manually, mark the image rectangles that contains one car. from previous, split the image in one or more ones, each one containing a ...


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There are different questions and even different lines of thought here. Let's go through them On resizing Why do we need to resize? To fit the network input which is fixed when nets are no Fully Convolutional Networks (FCN) What if my net is FCN? Still makes sense to resize to bound the dimension of the input features you want to detect (a person on a small ...


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There are many papers on this but the following is a good start: How to unwrap wine labels programmatically. The author includes source code in Python. You mentioned you do not want to do a panoramic view but that has more than one meaning. If I assume you mean you do not want to rotate the can while taking multiple photos, or you don't want to take ...


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It depends on your application. In case of text recognition, non-uniform kernels are used since the information about text is less on the horizontal axis and more on the vertical axis. If in your case it is applicable then, it will be good idea. But, if it is not you are better off using a smaller uniform kernel (2x2, maybe). You can also zero-pad your image ...


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When the original Neural Transfer paper was published, your question stayed unanswered for a while. The reason why the Gram matrix represents artistic style was not entirely clear. A satisfactory (in my opinion) explanation came with the "Demystifying Neural Style Transfer" paper. The basic idea is that you cannot just directly compare activations ...


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Equation 1 In normal Q-Learning your target is defined as $y_t = r_t + \gamma \mathrm{max_a}Q(s_{t+1}, a)$. Since you're training a regularized version, you construct the estimated value of the next state via averaging your estimations for each image augmentation. To turn this into the expected value over all $k$ transformations for the given state we need ...


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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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I'd argue that the legality depends strongly on how you access the data, and that automating this may put you in breach of facebook/instagram's terms of use for their data. It's not that accessing a set of images wouldn't count as fair use, it's that automatically scraping them probably breaches the terms of service. The most likely consequence, though, is ...


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I would look at table 1 of the original paper. While you're reading the alogorithm, try to really focus on Step 2 when you get to it. In summary, each feature is used to train it's own classifier. So in your example, the calculated features X1, X2, ... Xn you describe coorespond to apply some set of feature transforms f_1, f_2, ... f_n to a single image. ...


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In computer vision, the problem of filling missing parts of an image is called image inpainting; the subtask of filling the surroundings is called image outpainting in [1], which is your problem. The methods for solving the image outpainting problem are not mature according to the pre-print paper Image Outpainting and Harmonization using Generative ...


1

Stochastic augmentations are used to sample from all possible augmentations, when the dataset size of all possible augmentations would be too large. This is usually done on-demand, and as a result each training epoch should result in slighty different inputs. This approach can be beneficial in preventing overfitting when the variability in possible inputs is ...


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Image channels have nothing to do with machine learning, they are just part of computer image processing. A channel is a number per pixel. So most colour images are stored with red, green and blue channels, as you probably know. Some images are stored in greyscale with just one white channel. A RGB image is stored like this: pixel 0 red amount, pixel 0 green ...


1

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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So far, it seems this is more a software "integration" issue. One great tip from http://karpathy.github.io/2019/04/25/recipe/ is to visualize everything as often as you can during development. For data augmentation, try to visualize the image right before it enters your convnet. What I found is a bug can happen if your particular image transform ...


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Assuming that the image is blank everywhere but where the face is drawn... The first step is to scale the image to the mask. That doesn't require a detailed explanation here as it is too trivial a problem. Second, rotate the image by 90 degrees three times and save each one. Third, for the four versions of the image (the original and three rotations), do ...


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You can find an explanation here (github of the googleapi): My current understanding of a color's score is a combination of two things: What is the focus of the image? What is the color of that focus? For example, given the following image: The focus is clearly the cat, and therefore the color annotation for this image with the highest score (0.15) will ...


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The Problem We can see from the question that existing information on detection and classification in the small automotive vehicle domain has been located (in the form of two independent sets of vectors usable for machine training), and there is no already existing mapping or other correspondence between the elements of one set and the elements of the other. ...


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