Autoencoders are a type of feedforward neural network with identical input and output. In the 1980s, Geoffrey Hinton created autoencoders to solve unsupervised learning problems. They are neural networks that have been trained to replicate data from the input layer to the output layer. Autoencoders are used in a variety of applications, including pharmaceutical discovery, popularity prediction, and image processing.
Autoencoders work by Four steps. An autoencoder consists of three main components: the encoder, the code, and the decoder.
To begin, autoencoders are designed to take an input and transform it into a different representation. They then try to recreate the original input as precisely as possible.
Second, when a digit image is not clearly visible, it is fed into an autoencoder neural network.
Third, autoencoders encode the image first, then compress the input into a smaller representation.
Finally, the image is decoded by the autoencoder to produce the reconstructed image.
The following image demonstrates how autoencoders operate:
Autoencoders are used in unsupervised learning to learn efficient data codings. The goal is to learn a representation (encoding) for a set of data, typically for dimension reduction. The concept is becoming more popular for data generative models. Sparse autoencoders stacked inside deep neural networks have recently been used in some of the most powerful algorithms. We will attempt to build the following autoencoders: 1) A straightforward autoencoder based on a fully connected layer. 2) Autoencoder for sparse data. 3) a deep fully connected autoencoder 4) Convolutional deep autoencoder. 5) A model for image denoising. 6) Autoencoder for sequence-to-sequence. 7) Variational autoenoder.
Autoencoders are data compression algorithms that are data-specific, lossy, and self-learn from examples. Neural networks are used to implement the compression and decompression functions. 1) Data-specific: They will only be able to compress data that is similar to what they were trained on. They are not very generic about the data they hold, unlike standard compression algorithms. An autoencoder trained on images of faces, for example, would perform poorly when compressing images of trees because the features it learned are face-specific. 2) Lossy: The decompressed outputs will be inferior to the original inputs. 3) Auto learned: It is simple to train specialized instances of the algorithm that will perform well on a particular type of input. It does not necessitate new engineering, only appropriate training data.
Three Things To Build Autoencoders
To build autoencoder, we need three things: an encoding function, a decoding function, and a distance function between the amount of information loss between the compressed and decompressed representations of data (loss function).
1) How good are they at data compression? No, not at all. Because they are data-specific, they are generally inapplicable to real-world data compression problems. Genreization necessitates a large amount of training data. Future technological advancements may change this.
2) What are they useful for? For data visualization, data denoising and dimensionality reduction are used. Autoencoders can learn data projections that are more interesting than PCA or other basic techniques when given appropriate dimensionality and sparsity constraints.
3) What's all the fuss about autoencoders? For newcomers, it's absolutely fascinating. They have long been considered a potential avenue for learning useful representations without the use of labels. However, because they are self-supervised, they are not a true unsupervised learning technique.
Full code of Autoencoders using Tensorflow and Keras
First, download the MNIST.npz file here and see the following example of autoencoder code: