The Stacked Denoising Autoencoder (SDAE) is a type of deep learning model used for unsupervised pre-training and supervised fine-tuning. As an extension of the stacked autoencoder, it was introduced in 2008 by Vincent et al.
What is a Denoising Autoencoder?
Before diving into SDAE, it's important to understand what a denoising autoencoder (DAE) is. An autoencoder is a type of artificial neural network that learns to compress and decompress data. It consists of an encoder that compresses the input data into a lower-dimensional representation and a decoder that reconstructs the original data from the encoded representation.
DAE is a variation of autoencoders that is trained to remove noise from the input data before reconstructing it. This helps in learning useful features of the input data and making the model more robust to noisy data than a traditional autoencoder.
What is a Stacked Denoising Autoencoder?
SDAE is built by stacking multiple DAEs on top of each other. The output of the encoder of one DAE is fed as input to the next DAE in the stack. This allows the higher-level DAEs to learn more complex and abstract features from the lower-level DAEs' learned features, resulting in a deep learning model.
The unsupervised pre-training of SDAE consists of training each layer one-by-one as a DAE while using the output of the previous layer's encoder as the input. This helps in constructing a compressed and denoised representation of the data along with a hierarchy of features useful for further learning.
The supervised fine-tuning is done by adding a logistic regression layer on top of the SDAE output and then training the entire model as a multilayer perceptron. This is done to optimize the model for a specific supervised learning task, using the target class information during training.
Why use SDAE?
SDAE is beneficial for several reasons:
- The stacked structure helps in creating a hierarchy of learned features that can capture complex and abstract patterns from the input data.
- The denoising aspect of DAE helps in learning robust features that are more resistant to noise and variations in the input data.
- Unsupervised pre-training allows for semi-supervised and transfer learning tasks where only a small amount of labeled data is available. The model can be fine-tuned with supervised learning on the new task while keeping the previously learned features.
How is SDAE implemented?
Theano is a popular deep learning library used to implement SDAE. The implementation involves defining a class for DAE and then stacking multiple instances of this class on top of each other. The encoding part of each layer is used as the input to the next layer's DAE. Once all layers are pre-trained, a logistic regression layer is added on top to perform fine-tuning for supervised learning tasks.
Overall, SDAE is an effective deep learning model that can learn robust and abstract features from high-dimensional data while being resistant to noise and variations. It has several applications in fields such as computer vision, natural language processing, and speech recognition.
