Questions tagged [autoencoders]

For questions about autoencoders, a type of unsupervised artificial network for learning efficient data codings.

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What are the purposes of autoencoders?

Autoencoders are neural networks that learn a compressed representation of the input in order to later reconstruct it, so they can be used for dimensionality reduction. They are composed of an encoder ...
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Why is training all layers at a time effective for a multi-layer autoencoder?

This training of all layers of a CNN simultaneously is standard practice today. It is found in every CNN (AlexNet (2012), VGG, Inception, GANs, etc) and even pre-CNN networks such as Le et al. 2012. ...
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How to determine the number of hidden layers and units of a deep auto-encoder?

I am using a deep autoencoder for my problem. However, the way I choose the number of hidden layers and hidden units in a hidden layer is still based on my feeling. The size of the model that ...
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What is the difference between encoders and auto-encoders?

How are the layers in a encoder connected across the network for normal encoders and auto-encoders? In general, what is the difference between encoders and auto-encoders?
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Why is the variational auto-encoder's output blurred, while GANs output is crisp and has sharp edges?

I observed in several papers that the variational autoencoder's output is blurred, while GANs output is crisp and has sharp edges. Can someone please give some intuition why that is the case? I did ...
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Do Le et al. (2012) train all three autoencoder layers at a time, or just one?

Le et al. 2012 use a network of 1 billion parameters to learn neurons that respond to faces, cats, pedestrians, etc. without labels (unsupervised). Their network is built with three autoregressive ...
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What is the advantage of using a VAE over a deterministic auto-encoder?

What is the advantage of using a VAE over a deterministic auto-encoder? For example, assuming we have just 2 labels, a deterministic auto-encoder will always map a given image to the same latent ...
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What is the best loss function for convolution neural network and autoencoder? [closed]

What is the best choice for loss function in Convolution Neural Network and in Autoencoder in particular - and why? I understand that the MSE is probably not the best choice, because little ...
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