Add to ORKGThis M.Sc. thesis report investigates the application of one-class classification techniques to complex high-dimensional data. The aim of a one-class classifier is to separate target data from non-target data, but only a dataset containing target data is available for training. The issue with high-dimensional data is that it is difficult to perform density estimation due to the `curse of dimensionality'. Most conventional method for one-class classification rely on density estimation.This thesis focusses on the use of autoencoders and generative adversarial networks (GANs) for one-class classification problems involving image data. Autoencoders can learn encoding and decoding functions for samples from the target dataset. These encoding and...
We approach unsupervised clustering from a generative perspective. We hybridize Variational Autoenco...
This paper investigates data synthesis with a Generative Adversarial Network (GAN) for augmenting th...
Emerging data-driven technologies and big data analytics generate and deal with high-dimensional dat...
We introduce an algorithm for one-class classification based on binary classification of the target ...
Training deep learning models for images classification requires large amount of labeled data to ove...
One-class classification has important applications such as outlier and novelty detection. It is com...
Over the past few years, with the introduction of deep learning techniques such as convolution neura...
In machine learning research and application, multiclass classification algorithms reign supreme. Th...
This work examines the problem of increasing the robustness of deep neural network-based image class...
Abstract In this paper, we propose a novel deep learning-based feature learning architecture for obj...
Abstract One-class classification (OCC) poses as an essential component in many machine learning an...
Deep learning has achieved significant improvements in a variety of tasks in computer vision applica...
Semi-supervised learning has been gaining attention as it allows for performing image analysis tasks...
The thesis investigates various machine learning approaches to reducing data dimensionality, and stu...
Many applications of remote sensing only require the classification of a single land type. This is k...
We approach unsupervised clustering from a generative perspective. We hybridize Variational Autoenco...
This paper investigates data synthesis with a Generative Adversarial Network (GAN) for augmenting th...
Emerging data-driven technologies and big data analytics generate and deal with high-dimensional dat...
We introduce an algorithm for one-class classification based on binary classification of the target ...
Training deep learning models for images classification requires large amount of labeled data to ove...
One-class classification has important applications such as outlier and novelty detection. It is com...
Over the past few years, with the introduction of deep learning techniques such as convolution neura...
In machine learning research and application, multiclass classification algorithms reign supreme. Th...
This work examines the problem of increasing the robustness of deep neural network-based image class...
Abstract In this paper, we propose a novel deep learning-based feature learning architecture for obj...
Abstract One-class classification (OCC) poses as an essential component in many machine learning an...
Deep learning has achieved significant improvements in a variety of tasks in computer vision applica...
Semi-supervised learning has been gaining attention as it allows for performing image analysis tasks...
The thesis investigates various machine learning approaches to reducing data dimensionality, and stu...
Many applications of remote sensing only require the classification of a single land type. This is k...
We approach unsupervised clustering from a generative perspective. We hybridize Variational Autoenco...
This paper investigates data synthesis with a Generative Adversarial Network (GAN) for augmenting th...
Emerging data-driven technologies and big data analytics generate and deal with high-dimensional dat...