Autoencoders Explained with Working

Sharing is Caring

An autoencoder is a deep neural network that uses unsupervised learning. It is very effective in data representation such as encoding etc.  Data noise is reduced with the aid of autoencoders. Autoencoders enable you to reduce dimensionality and concentrate exclusively on areas of a true value by operating a few operations on data such as data compression, encoding it, and reproduction of data as an output. The output layer of an autoencoder neural network has the identical dimension as the input neurons. However, the input units should be equal to the output units in the output layer. An autoencoder, also known as a replicator neural network. It duplicates the data elements from the input layer towards the output layer in an unsupervised behavior.  The network is used by the autoencoders to recreate each input dimension. Using a neural network to replicate an input may appear simple, but doing so results in the input’s size being decreased to that of its smaller representation.  if we compare the input and output layers of the neural networks, the hidden layer consists of a few units. As a result, the intermediary layers store the input’s condensed representation. This condensed illustration of input is used to recreate the output.

Autoencoders Explained with Working

Parts of Autoencoder

A three-part autoencoder consists of the following:

Encoder: An encoder employ the architecture of a feedforward and fully connected network. It encodes an input image as a compressed representation in a smaller dimension after compressing the input into a latent space representation. The original image has been warped into a compressed form.

Code: The supplied input into the decoder is represented more briefly in this area of the network.

Decoder: The decoder is likewise a feedforward network with a topology resembling that of the encoder. This network is in charge of translating the input from the code back to its original dimensions.

The input is first compressed by the encoder and kept in the layer named “Code,” after which the decoder releases the original input’s compression from the code. The autoencoder’s primary goal is to produce an output that matches the input exactly. Keep in mind that the architecture of the encoder and decoder are identical. Although not required, this is frequently the case. The only prerequisite is that the input and output dimensions must match.

Types of Autoencoders

There are different types of autoencoders such as:

Variational encoders

Enigma and Welling from Qualcomm and Google proposed the variational autoencoder in 2013. When characterizing an experience in latent space, a variational autoencoder (VAE) offers a probabilistic way to do so. Since each latent state characteristic can be described by a probability distribution rather than a single value. Data compression, the production of synthetic data, and other uses are only a few of its many applications.

Also Read: Restricted Boltzmann Machine with Applications

A sort of neural network called an autoencoder learns the information embeddings from the dataset unsupervised. In essence, it consists of two parts: the first is an encoder, which resembles a convolution neural network with the exception of the final layer. The encoder’s goal is to use the dataset to learn effective data encoding, which it then transfers to a limiting architecture. The bottleneck layer’s latent space is used by the autoencoder’s decoder to regenerate images that are similar to those in the dataset. The loss function represents these outcomes as they backpropagate from the neural network.

In contrast to autoencoders, variational autoencoders offer a statistical method for representing the dataset’s samples in latent space. The encoder generates a probability distribution keeping itself in the bottleneck layer rather than a single output value as a result of variational autoencoding.

Similar to GANs, this kind of autoencoder can produce new images. Strong assumptions about the dispersion of latent variables are frequently made by variational autoencoder models. For latent representation learning, they employ a variational approach, which yields a further loss component and a particular estimator for the training procedure termed the Stochastic Gradient Variational Bayes estimator. A variational autoencoder often matches the training data more closely than a normal autoencoder in terms of the probability distribution of the salient vector. Any type of art can be generated using VAEs because they are far more adaptable and customizable in their creation behavior than GANs.

Convolution autoencoders

Convolutional Autoencoders (CAE) learn to transform the input into a collection of straightforward signals, which are then used to reconstruct the input. Additionally, by using CAE, we can alter the geometry or produce the image’s reflectance. Convolution layers and deconvolution layers are terms used to describe the encoder and decoder layers, respectively, in this form of the autoencoder.  Transpose convolution is another term for the deconvolution process.

Denoising autoencoder

The input image is given some noise, which the denoising autoencoders learn to eliminate. Consequently, it is prevented from copying the elements from the input layer to the output layer without discovering aspects of the data. During the training process of the model to recover the original, undistorted input, these autoencoders use a partially corrupted input. In order to remove the extra noise, the model learns a vector field to map the input data. The input data maps towards a lower-dimensional manifold that describes the natural data. The encoder will be able to extract the most crucial features this way and learn a more reliable illustration of the data.

Transformer autoencoder time series

Transformers are an architecture that was first developed in 2017 and is mostly utilized in the field of NLP. Its goal is to handle sequence-to-sequence tasks while easily managing long-range relationships. Without using convolution or sequence-aligned RNNs, it solely depends on self-attention to construct descriptions of its output and input. The primary functions of these Transformers include categorization, information extraction, answer production, summary, translation, text generation, etc.

The Transformer was suggested in the article Attention is All You Need. “The Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution.” Transforming input sequence data into output sequences is referred to as “transduction” in this context. The goal of the Transformer is to manage input and output relationships with complete attention and recurrence. Let’s look at the Transformer’s architecture below. Although it may seem scary, don’t worry; we’ll dissect it and comprehend it blocks by block.

The structure of the Transformer is beautifully depicted in the image up top. First, let’s concentrate exclusively on the encoder and decoder components. Currently, pay attention to the picture below. The Multi-Head Attention layer of the encoder block is followed by a layer of a feed-forward neural network. The decoder, however, features an additional Masked Multi-Head.

The blocks of encoders and decoders are in fact a number of similar encoders and decoders layered on top of one another. There is exactly the same number of elements both in the encoder layer and the decoder stack. A hyperparameter is the sum of the units of the encoder and decoder.

Deep autoencoders

Two symmetrical deep belief networks with very few layers such as four to five layers each make up a deep autoencoder. The encoding portion of the network is represented by one of the networks and the decoding portion by the second network. They can learn more advanced structures since they contain more layers than a straightforward autoencoder. The underlying units of deep belief networks, limited Boltzmann machines, make up the layers.

Chanllenges of using Autoencoders

  • Unsupervised techniques like autoencoders learn from their own facts rather than labels that were made by humans. This frequently means that in order for autoencoders to provide useful results, they require a large amount of clean data. If the set of data is too little, unclean, or noisy, they may produce inconsistent results.
  • To acquire the greatest results from autoencoders, data scientists must take into account the various categories that are provided in data collection.
  • Autoencoders are also lossy, which restricts their applicability in applications where system performance is significantly impacted by compression degradation.
  • A bottleneck layer, which is one of several layers of increasingly fewer neurons used in a standard autoencoder to encode the original input, is made up of neurons. If the bottleneck layer is too small, one risk is that the resultant algorithms might not take into account crucial problem dimensions.


What is an autoencoder bottleneck?

It is usually the lowest dimensional layer which is always hidden where the real encoding takes place.

What is the purpose of variational autoencoding?

Latent representations can be learned using the deep learning method known as variational autoencoders (VAEs). Additionally, they have been utilized to interpolate between phrases, produce state-of-the-art semi-supervised learning outcomes, and draw pictures. On VAEs, there are numerous online tutorials.

What does autoencoder loss mean?

L2 or L1 loss is the typical loss function applied in denoising autoencoders.

Leave a Comment