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We use MNIST which is a well known database of handwritten digits. Let's try noise reduction effect using the convolutional autoencoder. We add random noises to the MINST image data and use them...

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Dec 07, 2020 · Applications of Deep Neural Networks is a free 500 + page book by Jeff Heaton The contents are as below The download link is at the bottom of the page Introdu…

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MNIST seems to be the most popular choice for this type of problem, but I don't understand how people using it deal with the fact that new digits will likely not have the same anti-aliasing artifacts than the...

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Dec 01, 2020 · As with the original MNIST dataset, the task is to learn to classify the digits 0-9. Unlike the MNIST dataset, which consists of 28x28 images, each of these examples is a one-dimensional sequence of points. To generate an example, we begin with 10 digit templates and then randomly pad, translate, add noise, and transform them as shown above.

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The work from Diederik Kingma presents a conditional VAE [1] which combines an image label as part of the inference. Puting the math and derivation of the ELBO aside, the key change to the vanilla VAE’s architecture is to add a discriminator to classify the given MNIST digit and use this prediction as additional information to the decoder. This is expected to improve the generalization ability of the network through this automatic structuration by adding the noises. This network structuration was confirmed by experiments for MNIST digits classification via a deep neural network model.

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