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Outside of using a generated dataset to study machine learning, the typical purpose of a trained machine learning model is to process new inputs from some source. For a model to be effective, the training data set inputs and new inputs should be taken from the same distribution. The loss function used in training, combined with cross-validation to measure ...


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Data augmentation is usually rotating, cropping and translating images. And this makes sense if your network could meet these kind of images. If I take a digit recognition like LeNet, it is useless to complicate the task of the network by forcing it to learn rotated digits, which could lead to a more complex architecture and training and less accuracy in the ...


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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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