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Questions tagged [autoencoders]

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

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13 votes
4 answers
3k views

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 ...
nbro's user avatar
  • 40.8k
9 votes
4 answers
12k views

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 ...
Trect's user avatar
  • 269
2 votes
1 answer
75 views

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 ...
Josiah Yoder's user avatar
1 vote
1 answer
95 views

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. ...
Josiah Yoder's user avatar
1 vote
1 answer
2k views

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 ...
Truong Hoang's user avatar
10 votes
3 answers
2k views

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?
m2rik's user avatar
  • 333
7 votes
1 answer
6k views

Why doesn't VAE suffer mode collapse?

Mode collapse is a common problem faced by GANs. I am curious why doesn't VAE suffer mode collapse?
Trect's user avatar
  • 269
4 votes
2 answers
1k views

Can I apply reparametrization trick on "any" deep neural network?

I came across the "reparametrization trick" for the first time in the following paragraph from the chapter named Vector Calculus from the test book titled Mathematics for Machine Learning ...
hanugm's user avatar
  • 3,890
2 votes
2 answers
2k views

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 ...
Alex Marshall's user avatar
0 votes
1 answer
290 views

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 ...
Marko Zadravec's user avatar