Image Classification Using Deep Autoencoders

Gogoi, Munmi; Begum, Shahin Ara

doi:10.1109/iccic.2017.8524276

Cited by 17 publications

(11 citation statements)

References 5 publications

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“…Its weights may be fine tuned [9] or stay fixed during training. A simpler strategy can be found in [12], where a support vector machine (SVM) is trained on the output features of the encoder.…”

Section: Use Of Autoencoders For Classificationmentioning

confidence: 99%

Autoencoders

Bank,

Koenigstein,

Giryes

2020

Preprint

155

138

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An autoencoder is a specific type of a neural network, which is mainly designed to encode the input into a compressed and meaningful representation, and then decode it back such that the reconstructed input is similar as possible to the original one. This chapter surveys the different types of autoencoders that are mainly used today. It also describes various applications and use-cases of autoencoders.

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Section: Use Of Autoencoders For Classificationmentioning

confidence: 99%

Autoencoders

Bank,

Koenigstein,

Giryes

2020

Preprint

155

138

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“…Generally, autoencoders [16], [46] are a type of feedforward neural network, where the input is the same as the output. This method reduces the input feature space to a lowerdimensional coder, and then reconstructs the output from this representation.…”

Section: Deep Learning Using Autoencoder For Feature Extractionmentioning

confidence: 99%

“…Feature reduction is operated by two main methods: feature selection, which filters the irrelevant or redundant features from an original feature dataset and maintains a subset of the original feature dataset, and feature extraction, which creates a new feature dataset. Feature extraction identifies the dominant features or attributes of the dataset, through for example, principle components analysis [14], linear discriminant analysis (LDA) [15], and autoencoders [16]- [18]. By reducing the number of features that describe the dataset, feature extraction also increases the speed of machine learning techniques such as classification techniques.…”

Section: Introductionmentioning

confidence: 99%

Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

Jaruenpunyasak

Duangsoithong

2021

IEEE Access

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Deep learning algorithms are employed in many applications, especially in medical fields such as gait analysis and human pose detection for rehabilitation. However, creating the desired model with deep learning algorithms requires high memory and computing costs, which is problematic because deep learning technologies must be run on low-power devices such as edge computing equipment. To deal with these problems, feature reduction methods reduce the memory and energy costs. This paper presents an empirical analysis of deep learning with feature reduction. The method classifies foot images for knee rehabilitation using convolutional and dense autoencoders. The obtained results are compared with those of conventional methods (histograms of oriented gradients and local binary pattern algorithms). The features were classified and compared using support vector machine, k-nearest neighbor, and multilayer perceptron methods. The experimental results demonstrate that the conventional method uses fewer features than the deep learning method with higher accuracy because its algorithm projects pixels onto the histogram. In addition, using fewer features in deep learning layers maintains high accuracy, which is beneficial for edge computing implementations.

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“…An auto-encoder [ 1 , 3 , 62 , 63 , 64 ] is a neural network architecture that is trained to reconstruct its input [ 65 ] with the least possible amount of distortion. Their main purpose is to learn a compressed meaningful representation of the data that can be used for various applications, including clustering [ 66 ] and classification [ 67 , 68 ].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Learning of Disentangled Representation via Auto-Encoding: A Survey

Eddahmani¹,

Pham

Napoléon³

et al. 2023

Sensors

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In recent years, the rapid development of deep learning approaches has paved the way to explore the underlying factors that explain the data. In particular, several methods have been proposed to learn to identify and disentangle these underlying explanatory factors in order to improve the learning process and model generalization. However, extracting this representation with little or no supervision remains a key challenge in machine learning. In this paper, we provide a theoretical outlook on recent advances in the field of unsupervised representation learning with a focus on auto-encoding-based approaches and on the most well-known supervised disentanglement metrics. We cover the current state-of-the-art methods for learning disentangled representation in an unsupervised manner while pointing out the connection between each method and its added value on disentanglement. Further, we discuss how to quantify disentanglement and present an in-depth analysis of associated metrics. We conclude by carrying out a comparative evaluation of these metrics according to three criteria, (i) modularity, (ii) compactness and (iii) informativeness. Finally, we show that only the Mutual Information Gap score (MIG) meets all three criteria.

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Image Classification Using Deep Autoencoders

Cited by 17 publications

References 5 publications

Autoencoders

Autoencoders

Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

Unsupervised Learning of Disentangled Representation via Auto-Encoding: A Survey

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