This autoencoder is regularized for denoising application using a combined loss function which considers both mean square error and binary cross entropy. A dataset consisting of 59,119 letter images, which contains both English alphabets (upper and lower case) and numbers (0 to 9) is prepared ...
3. Stacked Autoencoder with Particle Swarm Optimization Preprocessing, segmentation, feature extraction, and classification are the primary stages in a CAD system. Despite this, the role of feature extraction is extremely important to the overall effectiveness of any classifier. After the image has bee...
Currently, most real-world time series datasets are multivariate and are rich in dynamical information of the underlying system. Such datasets are attracting much attention; therefore, the need for accurate modelling of such high-dimensional datasets is
In this paper, we apply Stacked Auto-encoder, one of the main types of deep networks, hot topic of machine learning recently, to spam detection and comprehensively compare its performance with other prevalent machine learning techniques those are commonl
Most of the examples I provide use an autoencoder structure: https://machinelearningmastery.com/lstm-autoencoders/ For an example implemented in the paper, see: https://machinelearningmastery.com/develop-encoder-decoder-model-sequence-sequence-prediction-keras/ Reply Jairo June 27, 2019 at 7:...
This article is a continuation of previous articles on deep neural network and predictor selection. Here we will cover features of a neural network initiated by Stacked RBM, and its implementation in the "darch" package.
Consider the following example: there is a three-stage truck maintenance pipeline. Initially, when a Truck comes to the maintenance service, it is added to the first stage and its status in the pipeline is set to "New". When the technicians start working
Majumdar, "Deep Dictionary Learning vs Deep Belief Network vs Stacked Autoencoder: An Empirical Analysis", ICONIP, pp. 337-344 2016.V. Singhal, A. Gogna, and A. Majumdar. "Deep Dictionary Learning vs. Deep Belief Network vs. Stacked Autoencoder: An Empirical Analysis." International ...
In this paper, a deep stacked auto-encoder (SAE) scheme followed by a hierarchical Sparse Modeling for Representative Selection (SMRS) algorithm is proposed to summarize dance video sequences, recorded using the VICON Motion capturing system. SAE’s main task is to reduce the redundant information...
In this paper, we propose a Stack Auto-encoder (SAE)-Driven and Semi-Supervised (SSL)-Based Deep Neural Network (DNN) to extract buildings from relatively low-cost satellite near infrared images. The novelty of our scheme is that we employ only an extrem