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The authors provide this image in their supplemental information: There, you can see their explanation. The convolutional layers encode the image into some latent space representation. The RNN operates in this latent space, generating a new latent space representation based on the previous observations. For any latent space representation, the decoder can ...


Let's say you are working with images that are normalized between [0,..,1] this would be the domain of x as referred in the paper right? No, the domain of X would be "images of [whatever they contain (e.g. dogs)] normalized between 0 and 1". Does this mean that I would have to sample my z from the domain of x, i.e: [0,..,1] ? No, they are both ...


Support Vector Machines are discriminative because they fit a hyperplane which separates two classes. So it learns a decision boundary which is the definition of discriminative methods.

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