Autoencoders Are Types Of Feedforward Neural Networks Where Target Values Are Equal To The Input Values. Autoencoders Are Used To Compress The Size Of Input Into Low Dimensionality. Then Reconstruct The Output From The Compressed Data.
Key Facts:
- It Is An Unsupervised Machine Learning Algorithm Similar To PCA.
- also, It Is A Neural Network.
- It Can Learn Non-Linear Transformations.
- It Can Use Convolutional Layers.
- thus, It Is More Proficient To Learn Many Layers.
- It Provides A Representation Of Each Layer As The Output.
- It however Applies Transfer Learning To Encoders And Decoders.
Components Of Autoencoder
Autoencoder Comprises Of Three Components: Encoder, Code, and Decoder
Encoder
Encoder Compresses The Input Into A Latent-Space Of Usually Smaller Dimension.
Code
Code Is The Representation Of Compressed Input Which Applies To The Decoder. This Part Of The Network Is Also refer As Bottleneck. It Balances Two Factors Such As Which Part Of Information To Be Taken And Which Part Of Information To Be Discarded.
Decoder
Decoder Restructures The Input Data From The Latent Space Representation.
Properties Of Autoencoders
- Data Specific – Compress Data Similar To What They Have Been Trained On.
- Lossy – The Decompressed Outputs Will Degrade Compare To The Original Inputs.
- Learned Automatically From Instances – It Is Easy To Train Specific Instances Of The Algorithm That Will Perform Well On A Particular Kind Of Input.
Four Hyperparameters Needs To Set Before Training An Autoencoder
- Code Size: It Is The Number Of nodes In The Middle Layer. For More Compression Code Size Should Be Small.
- Number Of Layers: The Architecture Of Autoencoders Consists Of Many Layers As We Want. In The Above Figure, Two Layers Are Use As Encoder And Decoder.
- Number Of Nodes Per Layers: thus, The Number Of Nodes Per Layer Reduces With Each Consequent Layer Of The Encoder And Rises Back In The Decoder. Also, The Structure Of The Decoder Layer Is Symmetric To The Structure Of The Encoder Layer.
- Loss Function: We Generally Use MSE (mean squared error) Or Binary Cross-Entropy. If The Input Values Are In The Range Of 0 And 1, Then We Commonly Use Cross-entropy.
Types Of Autoencoders
- Convolutional Autoencoder
The Convolution Operator Filters An Input Signal In Order To Extract Some Part Of It. Convolutional Autoencoders Use The Convolution Operator, Thus Signals Can Be Seen As A Sum Of Other Signals. They Learn To Encode The Input In A Set Of Signals And Then Try To Restructure The Input From Them.
- Contractive Autoencoder
Contractive Autoencoders Make The Feature Extraction Function (I.E. Encoder) Resist Infinitesimal Perturbations Of The Input. This Model Learns How To Contract A Neighborhood Of Inputs Into A Smaller Neighborhood Of Outputs.
- Sparse Autoencoder
Sparse Autoencoders Have Hidden Nodes Larger Than Input Nodes. They Have A Sparsity Penalty, A Value Close To Zero But Not Exactly Zero. Sparsity Penalty Is Specifically Applied On The Hidden Layer In Addition To The Reconstruction Error. This Prevents The Problem Of Overfitting.
Applications Of Autoencoders
Image Coloring:
Autoencoders Convert Any Black And White Image Into A Colored Image.
Feature Variation:
Autoencoders Extract Required Features From The Original Input Image And Then Generate Noise Free Output Image.
Watermark Removal:
Autoencoders Remove Watermarks From The Input Image.
Denoising Image:
A Denoising Autoencoder Is Trained To Reproduce The Clean Image From The Corrupted/Noisy Version.
Conclusion:
Now With This, We Came To The End Of This Autoencoder Article. In The Past Decade, Autoencoders Have Become An Emerging Field Of Research In Various Aspects. In This Article, We Have Discussed Definition, Key Facts, Components, Four Hyperparameters Autoencoders In Deep Learning. Additionally, We Have Seen Some Of The Types And Applications Of Autoencoders.
Written By: Preeti Bamane
Reviewed By: Rushikesh Lavate
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