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Feature extraction (FE) is not the same as representation learning (RL), but they are similar and related. You describe accurately what feature extraction typically refers to, i.e. the process of extracting (new) features from existing ones or raw data (e.g. images). For example, let's say you have a dataset associated with a car. You have only two features ...


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The number 50 is essentially just a guess based on results when compressing and/or generating data of a certain type. The variables such as "the three translations of the body, the three rotations of the head and the independent movements of the face's muscles" are examples only. There is no known formal map with well-defined parameters that ...


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We do it experimentally; you're able to look at what each layer is learning by tweaking various values throughout the network and doing gradient ascent. For more detail, watch this lecture: https://www.youtube.com/watch?v=6wcs6szJWMY&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv&index=12 it provides many methods used for understanding exactly what your ...


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The network architecture is relevant to this question. Convolutional neural network architectures enforce the building up of features because the neurons in earlier layers have access to a small number of input pixels. Neurons in deeper layers are connected (indirectly) to more and more pixels, so it makes sense that they identify larger and larger features. ...


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