Pattern Recognition: Invariance Learning in Convolutional Auto Encoder Network

Oyedotun, Oyebade K.; Dimililer, Kamil

doi:10.5815/ijigsp.2016.03.03

Cited by 16 publications

(3 citation statements)

References 13 publications

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“…Convolutional neural network (CNN) is a well-employed network for several tasks in machine vision and medicine [24, 25]. Generally, the CNN relies on architectural features which include the receptive field, weight sharing, and pooling operation to take into account the 2D characteristic of structured data such as images [26]. The concept of weight sharing for convolution maps drastically reduces model parameters; this has the important implications that the model is less prone to overfitting as compared to fully connected models of comparable size.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning

Dawud

Yurtkan

Öztoprak

2019

Computational Intelligence and Neuroscience

123

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In this paper, we address the problem of identifying brain haemorrhage which is considered as a tedious task for radiologists, especially in the early stages of the haemorrhage. The problem is solved using a deep learning approach where a convolutional neural network (CNN), the well-known AlexNet neural network, and also a modified novel version of AlexNet with support vector machine (AlexNet-SVM) classifier are trained to classify the brain computer tomography (CT) images into haemorrhage or nonhaemorrhage images. The aim of employing the deep learning model is to address the primary question in medical image analysis and classification: can a sufficient fine-tuning of a pretrained model (transfer learning) eliminate the need of building a CNN from scratch? Moreover, this study also aims to investigate the advantages of using SVM as a classifier instead of a three-layer neural network. We apply the same classification task to three deep networks; one is created from scratch, another is a pretrained model that was fine-tuned to the brain CT haemorrhage classification task, and our modified novel AlexNet model which uses the SVM classifier. The three networks were trained using the same number of brain CT images available. The experiments show that the transfer of knowledge from natural images to medical images classification is possible. In addition, our results proved that the proposed modified pretrained model “AlexNet-SVM” can outperform a convolutional neural network created from scratch and the original AlexNet in identifying the brain haemorrhage.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning

Dawud

Yurtkan

Öztoprak

2019

Computational Intelligence and Neuroscience

123

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“…However, it is difficult to preserve the spatial locality information of images with this structure. Therefore, a convolutional auto-encoder was proposed [6], and some studies applied to various problems [28], [29], [30].…”

Section: Related Workmentioning

confidence: 99%

DAQN: Deep Auto-encoder and Q-Network

Kimura

2018

Preprint

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The deep reinforcement learning method usually requires a large number of training images and executing actions to obtain sufficient results. When it is extended a real-task in the real environment with an actual robot, the method will be required more training images due to complexities or noises of the input images, and executing a lot of actions on the real robot also becomes a serious problem. Therefore, we propose an extended deep reinforcement learning method that is applied a generative model to initialize the network for reducing the number of training trials. In this paper, we used a deep q-network method as the deep reinforcement learning method and a deep auto-encoder as the generative model. We conducted experiments on three different tasks: a cart-pole game, an atari game, and a real-game with an actual robot. The proposed method trained efficiently on all tasks than the previous method, especially 2.5 times faster on a task with real environment images.

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“…An extension of ordinary autoencoders are the so called convolutional autoencoders (CAEs), which have been developed primarily for spatial field data compression but have proven particularly useful in several applications dealing with high-dimensional data sets. Some of these applications pertain to the fields of computer vision [31], pattern recognition [32] and time series data prediction [33]. Similarly to ordinary AEs, CAEs also consist of an encoder and a decoder part but they are constructed using different types of layers, called convolutional and deconvolutional layers [34].…”

Section: Introductionmentioning

confidence: 99%

Non-intrusive Surrogate Modeling for Parametrized Time-dependent PDEs using Convolutional Autoencoders

Nikolopoulos,

Kalogeris,

Papadopoulos

2021

Preprint

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This work presents a non-intrusive surrogate modeling scheme based on machine learning technology for predictive modeling of complex systems, described by parametrized time-dependent PDEs. For this type of problems, typical finite element solution approaches involve the spatiotemporal discretization of the PDE and the solution of the corresponding linear system of equations at each time step. Instead, the proposed method utilizes a convolutional autoencoder in conjunction with a feed forward neural network to establish a low-cost and accurate mapping from the problem's parametric space to its solution space. The aim is to evaluate directly the entire time history solution matrix through these interpolation schemes. For this purpose, time history response data are collected by solving the high-fidelity model via FEM for a reduced set of parameter values. Then, by applying the convolutional autoencoder to this data set, a low-dimensional representation of the high-dimensional solution matrices is provided by the encoder, while the reconstruction map is obtained by the decoder. Using the latent representation given by the encoder, a feed-forward neural network is efficiently trained to map points from the problem's parametric space to the compressed version of the respective solution matrices. This way, the encoded time-history response of the system at new parameter values is given by the neural network, while the entire high-dimensional system's response is delivered by the decoder. This approach effectively bypasses the need to serially formulate and solve the governing equations of the system at each time increment, thus resulting in a significant computational cost reduction and rendering the method ideal for problems requiring repeated model evaluations or 'real-time' computations. The elaborated methodology is demonstrated on the stochastic analysis of time-dependent PDEs solved with the Monte Carlo method, however, it can be straightforwardly applied to other similartype problems, such as sensitivity analysis, design optimization, etc.

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Pattern Recognition: Invariance Learning in Convolutional Auto Encoder Network

Cited by 16 publications

References 13 publications

Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning

Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning

DAQN: Deep Auto-encoder and Q-Network

Non-intrusive Surrogate Modeling for Parametrized Time-dependent PDEs using Convolutional Autoencoders

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