Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 11 of 11 for “"Generative Adversarial Networks (GAN)"”.
-
ASSET PRICING OPTIMIZATION THROUGH GENERATIVE ADVERSARIAL NETWORKS
… we study the asset pricing optimisation through Generative Adversarial Networks (GAN). We have demonstrated that shallow learning can deliver similar performance for test data as compared to deep learning considered in the literature, with the added benefit of mitigating common challenges such as …
-
Using Machine Learning Techniques to Model Encoder/Decoder Pair for Non-invasive Electroencephalographic Wireless Signal Transmission
… techniques, particularly those borrowed from Generative Adversarial Networks (GAN) and other Deep Neural Networks, to minimize data transmission and ensure robustness against data degradation. The study highlighted the crucial role of machine learning in self-adjusting and isolating essential …
-
Deep Generative Models and Biological Applications
… in a wide variety of applications. </p><p>Generative models provide an excellent manipulation method for training from rich available unlabeled data set and sampling new data points from underlying high-dimensional probability distributions. </p><p>The recent proposed Variational …
-
A Novel Deep Learning Approach for Emotion Classification
Neural Networks are at the core of computer vision solutions for various applications. With the advent of deep neural networks Facial Expression Recognition (FER) has been a very ineluctable and challenging task in the field of computer vision. Micro-expressions (ME) have been quite prominently …
-
Generative modelling and adversarial learning
… of real-world data, such as natural images. Generative adversarial networks (GAN), which are based on the adversarial learning paradigm, are one of the main types of methods for deriving generative models from complicated real-world data. GAN and its variants use a generator to synthesise …
-
Deep generative models for speech editing
Generative models are very useful for generating and modifying natural-sounding speech in various speech processing tasks such as speech synthesis, speech enhancement, and voice conversion. There are two ways that the generative models can help in naturalness for speech processing. The first way is …
-
Polymorphic Adversarial DDoS attack on IDS using GAN
… in preventing malicious traffic from penetrating networks. IDS have been rapidly enhancing their detection ability using ML algorithms. As a result, attackers look for new methods to evade the IDS. Polymorphic attacks are favorites among the attackers as they can bypass the IDS. GAN is a method …
-
From in vitro to in vivo imaging: advanced computational methods for multi-scale biomedical image analysis
L'abstract è presente nell'allegato / the abstract is in the attachment
-
Audio super-resolution with deep neural networks
This thesis reports various attempts at applying generative deep neural networks to audio for the task of recovering a high quality audio signal when given a low sample rate signal. Our experiments show that deep networks are able to discover patterns in speech and music signals by working in both …
-
Individual recognition from low quality and occluded images and videos using GAN
… algorithms, like ARCNN, IRCNN, SRGAN, etc., provide excellent results in the field of image enhancement. However, regarding face recognition, these algorithms fail miserably. Thus, we designed an algorithm which is specific to the enhancement of faces for better recognition …
-
Computer vision based classification of fruits and vegetables for self-checkout at supermarkets
The field of machine learning, and, in particular, methods to improve the capability of machines to perform a wider variety of generalised tasks are among the most rapidly growing research areas in today’s world. The current applications of machine learning and artificial intelligence can be …