Table of contents
1.
What are Autoencoders?
2.
Sparse Autoencoder
3.
L1-Regularization Sparse
4.
Frequently Asked Questions
5.
Key Takeaways
Last Updated: Mar 27, 2024

Sparse Autoencoder

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What are Autoencoders?

Autoencoder is Feed-Forward Neural Networks where the input and the output are the same. Autoencoders encode the image and then decode it to get the same image. The core idea of autoencoders is that the middle layer must contain enough information to represent the input.

There are three important properties of autoencoders:

1. Data Specific: We can only use autoencoders for the data that it has previously been trained on. For instance, to encode an MNIST digits image, we’ll have to use an autoencoder that previously has been trained on the MNIST digits dataset.

2. Lossy: Information is lost while encoding and decoding the images using autoencoders, which means that the reconstructed image will have some missing details compared to the original image.

3. Unsupervised: Autoencoders belong to the unsupervised machine learning category because we do not require explicit labels corresponding to the data; the data itself acts as input and output.

 

Caption: Architecture of an Autoencoder

 

Sparse Autoencoder

Sparse Autoencoders are one of the valuable types of Autoencoders. The idea behind Sparse Autoencoders is that we can achieve an information bottleneck (same information with fewer neurons) without reducing the number of neurons in the hidden layers. The number of neurons in the hidden layer can be greater than the number in the input layer.

We achieve this by imposing a sparsity constraint on the learning. According to the sparsity constraint, only some percentage of nodes can be active in a hidden layer. The neurons with output close to 1 are active, whereas the neurons close to 0 are in-active neurons.

More specifically, we penalize the loss function such that only a few neurons are active in a layer. We force the autoencoder to represent the input information in fewer neurons by reducing the number of neurons. Also, we can increase the code size because only a few neurons are active, corresponding to a layer.

 

Caption: Sparse Autoencoder

Source: www.medium.com

 

In Sparse autoencoders, we use L1 regularization or KL-divergence to learn useful features from the input layer.

 

L1-Regularization Sparse

L1-Regularization is one of the most famously used regularization methods in Machine Learning. In L1-Regularization, we use the magnitude of coefficients as the penalty term.

 

 

Plotting the graph of L1 and the derivative of L1, we’ll get:

 

Graph: L1 = ||w||

 

Graph: Derivative of L1 with respect to w

 

For L1-Regularization, the derivative is either 1 or -1 (except when w=0), which means regardless of the value of w, L1-Regularization will always push w towards zero with the same step size.

 

Frequently Asked Questions

Q1. What are the essential components of an autoencoder?

Ans. Every encoder has three components:

  1. Encoder
  2. Code
  3. Decoder

 

Q2. What are the three properties of Autoencoders?

Ans. The three properties of autoencoders are:

  1. Data Specific,
  2. Lossy (The reconstructed images loses details when compared to the original image),
  3. Learn automatically from the data examples.

 

Q3. Autoencoders belongs to which category of Machine Learning?

Ans. Autoencoders belong to the unsupervised machine learning category; they do not need explicit labels for training because input and output are the same.

 

Q4. What are the different types of Autoencoders?

Ans. There are seven types of Autoencoders:

  1. Sparse Autoencoder
  2. Deep Autoencoder
  3. Convolutional Autoencoder
  4. Contractive Autoencoder
  5. Variational Autoencoder
  6. Denoising Autoencoder
  7. Undercomplete Autoencoder

 

Q5. What is Denoising Autoencoder?

Ans. The idea of the Denoising autoencoder is that we add random noise instances in the input images and then ask the autoencoder to recover the original image from the noisy one. The autoencoder has to subtract the noise and only output the meaningful features.

Key Takeaways

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