Auto encoders Tutorial: A Beginner’s Guide to Auto encoders

What are auto encoders?

Auto encoders are unsupervised artificial neural network machine learning algorithm. It is used to reduce the data to lower dimension and smaller representations. Original data can be reconstructed from the compressed data. It’s based-on encoder-decoder technique.

A similar machine learning algorithm is PCA but Auto encoders are preferred to PCA because of the following reasons:

1. Auto encoders can be both linear or non-linear transformation of data depending on the kind of activation function whereas PCA is linear transformation.
2. PCA goes for small datasets and auto encoders goes for larger datasets.
3. Auto encoder on linear activation mode with single layer shows same performance as PCA but Auto encoder on non-activation function with multiple layers will be over fitting and can be controlled careful designing and regularization.

This article will help you gain knowledge about the needs, applications, architecture, properties, hyper parameters and types of Auto encoders. Artificial Intelligence Course

Applications of Auto encoders

Dimensionality Reduction: The reframed image will be the same as the input only with reduced dimensions. It will produce a similar image with reduced pixel size.

Watermark Removal: Auto encoders are used to remove any specific object from a video or images. It can remove watermarks from images and documents.

Image Coloring: Auto encoders can transform black and white images into a colorful image.

Input Image – Encoder – Bottleneck Layer – Decoder – Output Image

Denoising  Image: Most of the time the input seen by the auto encoder is not original input but a corrupted version. The denoising auto encoder reconstructs the original input from the corrupted and noisy version.

Feature Variation: Only required features will be extracted from the image. Auto encoders generates the output by removing unnecessary features, interruption and noise.

Layers of Auto Encoders

1. Encoder
2. Code
3. Decoder

• Encoder is that part of network which compresses the input into different small layers. The compressed form will be reduced in dimension. The compressed image will be a bit distorted version of the original image.
• Code is that part of network which is the compressed input provided by encoder to decoder.
• Decoder is that part of the network which decodes the encoded image or input back to its original form or dimension.

Bottleneck is the layer between the encoder and decoder. It decides which part of the data is relevant and which are irrelevant.

Properties and Hyper parameters Properties of Auto encoders:

1. Compacting: The output after being compressed then decompressed will result in degraded form as compared to the original input.
2. Data Specific: Auto encoders can compress data which they have trained to compress.

Hyper parameters of Auto encoders

1. Code size: Number of nodes present in the middle layer is known as code size.

The larger the code size less will be the compression and the smaller the code size more will be the compression.

2. Number of layers: Auto encoders consists of as many layers as needed.
3. Number of nodes per layer: The number of nodes goes on decreasing with each increasing layer of the encoder and it increases with each decreasing layer of the decoder.
4. Loss function: The goal is to minimize loss. Here loss means the main objective which the auto encoder wants to reach.

Types of auto encoder

1. Convolution Auto encoders: Convolutional auto encoders are the encoders which use convolution operator to exploit the observation that a signal can be seen as a sum of other signals. They encode the input into some simple signals and then reconstruct the input from the simple signals.

Use cases of convolution auto encoders are: image reconstruction, latent space clustering, image colorization and generating higher resolution images.

2. Sparse Auto encoders: Sparse auto encoders encodes and decodes input without reducing the number of nodes in the hidden layer. They construct a loss function.
3. Deep Auto encoders: It’s just an extension of the normal auto encoder. The first and second layer of the deep auto encoder is used for first order features in raw input and second order features corresponding to patterns in the appearance of first-order features respectively.

Use cases of deep auto encoder are: data compression, topic modeling & information retrieval and image search.

4. Contractive Auto encoders A contractive auto encoder is another type of sparse auto encoder; it penalizes the weights of each hidden layer.

Now, you have the basic idea about the Auto encoder, its application, properties, layers, hyper parameters and types of Auto encoders. You all now know what is it like to compress and decompress inputs.

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