What are stacked autoencoders?
A stacked autoencoder is a neural network consist several layers of sparse autoencoders where output of each hidden layer is connected to the input of the successive hidden layer. The learned data from the previous layer is used as an input for the next layer and this continues until the training is completed.
What are deep autoencoders?
A deep autoencoder is composed of two, symmetrical deep-belief networks that typically have four or five shallow layers representing the encoding half of the net, and second set of four or five layers that make up the decoding half.
What do Undercomplete autoencoders have?
The objective of undercomplete autoencoder is to capture the most important features present in the data. Undercomplete autoencoders have a smaller dimension for hidden layer compared to the input layer. This helps to obtain important features from the data.
How are deep autoencoders used for real-valued data?
Deep autoencoders can be used for other types of datasets with real-valued data, on which you would use Gaussian rectified transformations for the RBMs instead. Final encoding layer is compact and fast. Chances of overfitting to occur since there’s more parameters than input data.
Why do undercomplete autoencoders have a smaller dimension?
Undercomplete autoencoders have a smaller dimension for hidden layer compared to the input layer. This helps to obtain important features from the data. It minimizes the loss function by penalizing the g (f (x)) for being different from the input x.
Which is better contractive or denoising autoencoder for hidden layer?
Frobenius norm of the Jacobian matrix for the hidden layer is calculated with respect to input and it is basically the sum of square of all elements. Contractive autoencoder is a better choice than denoising autoencoder to learn useful feature extraction. This model learns an encoding in which similar inputs have similar encodings.
What is the sparsity penalty in sparse autoencoder?
Sparse autoencoders have a sparsity penalty, a value close to zero but not exactly zero. Sparsity penalty is applied on the hidden layer in addition to the reconstruction error. This prevents overfitting. They take the highest activation values in the hidden layer and zero out the rest of the hidden nodes.