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Feedforward neural networks trained to reconstruct their own input. Usually one of the hidden layers is a "bottleneck", leading to encoder->decoder interpretation.

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Why binary crossentropy can be used as the loss function in autoencoders? [duplicate]

I was wondering why binary crossentropy can be used as the loss function in autoencoders trained on (normalized) images, e.g. here or this paper? …
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Why binary crossentropy can be used as the loss function in autoencoders?

That's simply wrong since in most of the machine learning models (including autoencoders) we are trying to minimize a loss/cost function. …
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