stacked convolutional sparse denoising autoencoder

Stacked Convolutional Sparse Denoising Autoencoder (SCSDA) is a type of deep neural network architecture used for unsupervised learning of feature representations from input data. It is an extension of the Convolutional Sparse Denoising Autoencoder (CSDA) which uses convolutional layers to learn features from input data.

The SCSDA consists of multiple layers of convolutional sparse denoising autoencoders stacked on top of each other. Each layer is trained to learn a set of features from the output of the previous layer. The input to the first layer is the raw input data, and the output of the last layer is the learned feature representation.

In each layer of the SCSDA, the convolutional filters are trained to extract local features from the input data. The sparse coding and denoising of the autoencoder help to extract more robust and meaningful features. The use of multiple layers allows the network to learn more complex features by combining simpler features learned in previous layers.

SCSDA has been used in various applications such as image classification, speech recognition, and natural language processing.