example/autoencoder/README.md - mxnet - Git at Google

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 # Example of a Convolutional Autoencoder

 Autoencoder architectures are often used for unsupervised feature learning. This [link](http://ufldl.stanford.edu/tutorial/unsupervised/Autoencoders/) contains an introduction tutorial to autoencoders. This example illustrates a simple autoencoder using a stack of convolutional layers for both the encoder and the decoder.


 ![](https://cdn-images-1.medium.com/max/800/1*LSYNW5m3TN7xRX61BZhoZA.png)

 ([Diagram source](https://towardsdatascience.com/autoencoders-introduction-and-implementation-3f40483b0a85))


 The idea of an autoencoder is to learn to use bottleneck architecture to encode the input and then try to decode it to reproduce the original. By doing so, the network learns to effectively compress the information of the input, the resulting embedding representation can then be used in several domains. For example as featurized representation for visual search, or in anomaly detection.

 ## Dataset

 The dataset used in this example is [FashionMNIST](https://github.com/zalandoresearch/fashion-mnist) dataset.

 ## Variational Autoencoder

 You can check an example of variational autoencoder [here](https://gluon.mxnet.io/chapter13_unsupervised-learning/vae-gluon.html)
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	# Example of a Convolutional Autoencoder

	Autoencoder architectures are often used for unsupervised feature learning. This [link](http://ufldl.stanford.edu/tutorial/unsupervised/Autoencoders/) contains an introduction tutorial to autoencoders. This example illustrates a simple autoencoder using a stack of convolutional layers for both the encoder and the decoder.


	![](https://cdn-images-1.medium.com/max/800/1*LSYNW5m3TN7xRX61BZhoZA.png)

	([Diagram source](https://towardsdatascience.com/autoencoders-introduction-and-implementation-3f40483b0a85))


	The idea of an autoencoder is to learn to use bottleneck architecture to encode the input and then try to decode it to reproduce the original. By doing so, the network learns to effectively compress the information of the input, the resulting embedding representation can then be used in several domains. For example as featurized representation for visual search, or in anomaly detection.

	## Dataset

	The dataset used in this example is [FashionMNIST](https://github.com/zalandoresearch/fashion-mnist) dataset.

	## Variational Autoencoder

	You can check an example of variational autoencoder [here](https://gluon.mxnet.io/chapter13_unsupervised-learning/vae-gluon.html)