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I personally can’t think of any good reason to apply data augmentation before splitting the dataset, though one may exist. The issue is that if you augment first and split later, you risk introducing unwanted correlations between your training and test datasets. In the paper you linked it sounds as if the training data is all procedurally derived from the ...


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In my personal experience, that depends. Augmenting data for training purposes is valid, and can even improve performance, as you may be aware. For testing purposes, it may be valid. Let me give you two examples when that may be the case: Facial Recognition. Imagine that you have an augmentation function that can change the face pose (left/right pose, for ...


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I don't think you need to go for aggregation -- this looks like a job for VARIMA, the vector-version of ARIMA. In ARIMA, the output of the sequence at time $t$, which can be notated $X_t$, is a function of the past inputs $\{X_1, X_2, \dots, X_{t-1}\}$. For a univariate $AR(k)$ process, the corresponding ARIMA model is given by $$ X_t - \sum_{i=1}^k \alpha_i ...


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You can see some labels at https://www.tensorflow.org/datasets/catalog/emnist. It goes like this: ‘0’-‘9’ are 0-9 ‘A’-‘Z’ are 10-35 ‘a’-‘z’ are 36-61


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You can claim to use a real-world dataset, you would just need to specify that some values were interpolated. Do you have to have the inter-mediate values though? By the looks of it, each "region" was only measured every 2 hours, so I would just keep it that way and just have the resolution be 2 hours. It doesn't have to be hourly, and probably ...


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I know at least one example where the rank of the dataset (more specifically, the rank of a matrix that is computed from the design matrix, i.e. the matrix with your data, which I will describe more in detail below) can have an impact on the number of solutions that you can have or how you find those solutions. I am thinking of linear regression. So, in ...


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Does your question pertain to general data augmentation? That is already in heavy use- using transformations while training is very common, and over several epochs the network benefits from learning the new representations. The transformations are applied to all classes, with a probability of transformation ( horizontal flip, for example) specified by the ...


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Modules you import in python for machine learning, like tensorflow or sklearn or pytorch are open source. They are not hosted on any particular environment. Instead, as you import module, you get raw code, which you may modify as per your requirement. Thus, as you can see, there is no way for them to steal your data while you are using these modules on your ...


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