srch

Sparse auto-encoders (SAEs) are auto-encoders that impose constraints on the parameters so that they are sparse (i.e. zero or close to zero) ...

... Convolutional sparse coding (CSC) and convolutional auto-encoders (CAEs) extend the original patch-based models to cope with ...

16.12.2021 · This paper introduces a new intelligent fault diagnosis method based on stack pruning sparse denoising autoencoder and convolutional neural network (sPSDAE-CNN). This method processes the original input data by using a stack denoising autoencoder. Different from the traditional autoencoder, stack pruning sparse denoising autoencoder includes a fully …

Oct 30, 2019 · killamocingbird / Convolutional-Sparse-Autoencoder Public. Notifications Fork 0; Star 0. 0 stars 0 forks Star Notifications Code; Issues 0; Pull requests 0; Actions ...

01.10.2020 · The proposed stacked convolutional sparse denoising autoencoder. In this section, the underwater noise model is introduced firstly, then, on the basis of SSDA, the SCSDA model is proposed and its structure is described in detail. Finally, the characteristics of SCSDA model is illustrated in different underwater noise models. 2.1. Underwater ...

To address these problems, we propose a semi-supervised deep learning framework called stacked convolutional sparse auto-encoder, ...

14.07.2019 · Autoencoder is an artificial neural network used to learn efficient data codings in an unsupervised manner. There are 7 types of autoencoders, namely, Denoising autoencoder, Sparse Autoencoder, Deep Autoencoder, Contractive Autoencoder, Undercomplete, Convolutional and Variational Autoencoder.

08.10.2021 · The technique here is based on the convolutional and sparse autoencoder learning algorithms and is called sparse convolutional autoencoder. Unlike the typical convolutional neural network (CNN) that changes the size of the data itself in the intermediate layers, flow field data size is not changed in the learning process of this method and only the numbers of …

Convolutional Autoencoder is a variant of Convolutional Neural Networks that are used as the tools for unsupervised learning of convolution filters. They are generally applied in the task of image reconstruction to minimize reconstruction errors by learning the optimal filters they can be applied to any input in order to extract features.

This paper aims to improve the feature learning in Convolutional Networks (Convnet) by capturing the structure of objects. A new sparsity ...

23.11.2021 · Sparse Representation Convolutional Autoencoder for Feature Learning of Vibration Signals and Its Applications in Machinery Fault Diagnosis Abstract: Vibration signals are widely utilized in many fields, which can reflect machine health state.

The architecture of our sparse Convolutional Autoencoder (CAE). The CAE minimizes image reconstruction error. The reconstructed image patch is a pixel-wise sum- mation of two intermediate reconstructed image patches: the background and the foreground.

Deep learning-based methods have been recently reported to suitably address the missing data problems in various fields. To explore the performance of deep learning for genotype imputation, in this study, we propose a deep model called a sparse convolutional denoising autoencoder (SCDA) to impute missing genotypes.

[5] Sparse autoencoder, Andrew Ng, etc, 2011 [6] Stacked Convolutional Auto-Encoders for Hierarchical Feature, Jonathan Masci, Jurgen Schmidhuber etc, 2011 [7] Unsupervised Learning of Video Representations using LSTMs, Nitish Srivastava etc, 2015 [8] Auto-encoding variational bayes, Diederik Kingma etc, ICLR 2014

Abstract: Convolutional sparse coding (CSC) can model local connections between image content and reduce the code redundancy when compared ...

Nov 23, 2021 · Hence, a new deep neural network (DNN), sparse representation convolutional autoencoder (SRCAE) is proposed to extract impulsive components of vibration signals for machinery fault diagnosis in an unsupervised manner.

We propose a sparse Convolutional Autoencoder (CAE) for simultaneous nucleus detection and feature extraction in histopathology tissue images. Our CAE detects and encodes nuclei in image patches in tissue images into sparse feature maps that encode both the location and appearance of nuclei.

We describe a way to train convolutional autoencoders layer by layer, where in ... directly learning a sparse convolutional autoencoder.

Sparse Autoencoders • Limit capacity of autoencoder by adding a term to the cost function penalizing the code for being larger • Special case of variational autoencoder • Probabilistic model • Laplace prior corresponds to L1 sparsity penalty • Dirac variational posterior

Sparse autoencoder. 1 Introduction. Supervised learning is one of the most powerful tools of AI, and has led to automatic zip code recognition, ...

To explore the performance of deep learning for genotype imputation, in this study, we propose a deep model called a sparse convolutional denoising autoencoder (SCDA) to impute missing genotypes. We constructed the SCDA model using a convolutional layer that can extract various correlation or linkage patterns in the genotype data and applying a ...

In this work, we proposed a method called denoising sparse convolutional autoencoder (DSCAE) to defense against the adversarial ...

autoencoder is a non-symmetric autoencoder where the encoder typically consists of a stack of several ReLU convolutional layers (e.g., 5× 5filters) and the decoder is a linear deconvolutional layer of larger size (e.g., 11× 11filters). We chose to use a deep encoder with smaller filters (e.g.,

The technique here is based on the convolutional and sparse autoencoder learning algorithms and is called sparse convolutional autoencoder.