Physics-informed Data Augmentation for Human Radar Signatures

In recent years, neural networks (NNs) have seen a surge in popularity due to their ability to model complex patterns and relationships in data. One of the challenges of using NNs is the requirement for large amounts of labelled data to train the model effectively. In many real-world applications such as radar, labelled data may be scarce due to...

Permutation-Invariant Tabular Data Synthesis

Tabular data synthesis is a promising approach to circumvent strict regulations on data privacy. Although the state-of-the-art tabular data synthesizers, e.g., table-GAN, CTGAN, TVAE, and CTAB-GAN, are effective at generating synthetic tabular data, they are sensitive to column permutations of input data. In this work, we conduct an impact and...

Building massing generation using GAN trained on Dutch 3D city models

Despite being relatively novel, generative adversarial networks (GAN) have already been appropriated for application to several problems within the field of architectural and urban generative design. However, the preceding GAN based models for building massing generation make use of only simplified and two dimensional representation of the built...

Iterative training with human rated images to improve GAN generated image aesthetics: Effects of dataset size and training length

Generative Adversarial Networks (GANs) brought rapid developments in generating synthetic images by mimicking structures in the training data. With the list of application of GANs growing drastically, it has lately become an exciting technology to explore for designers to communicate their ideas and arts through technology and create engaging...

Using Decision Trees produced by Generative Adversarial Imitation Learning to give insight into black box Reinforcement Learning models

Machine learning models are increasingly being used in fields that have a direct impact on the lives of humans. Often these machine learning models are black-box models and they lack transparency and trust which is holding back the implementation. To increase transparency and trust this research investigates whether imitation learning,...

Neural Network Based Generation of Reactionary Dance Improvisations: "M’AI have this dance?" or "How to train your DRA-GAN"

This MSc. thesis presents the extensive research conducted on designing neural networks capable of generating reactionary dance motions. The research was conducted in collaboration with Stichting Another kind of Blue (AKOB) and CompactCopters UG (CC). It is set within the industry context of the ’AI-man’ project, developed...

Time series synthesis using GANs - A take on DoppelGANger

With a growing need for data comes a growing need for synthetic data. In this work we reproduce the results of DoppelGANger [16] in synthesising time series data with metadata. We identify a key issue in the comparison made in [16] of DoppelGANger to TimeGAN, RNNs, AR and HMM models, which creates a new avenue of time series synthesis using GANs...

Time Series Synthesis using Generative Adversarial Networks

Generative Adversarial Networks are widely used as a tool to generate synthetic data and have previously been applied directly to time-series data. However, relying solely on the binary adversarial loss is not sufficient to ensure the model learns the temporal dynamics of the data. TimeGAN [14] introduces an additional reconstruction and...

Image-to-Image Translation of Synthetic Samples for Rare Classes

The natural world is long-tailed: rare classes are observed orders of magnitudes less frequently than common ones, leading to highly-imbalanced data where rare classes can have only handfuls of examples. Learning from few examples is a known challenge for deep learning based classification algorithms, and is the focus of the field of low-shot...

Investigating the performance of Generative Adversarial Networks on Fabric Pattern Generation

Generative adversarial networks (GANs) are a popular method for image generation, capable of state-of-the-art. Despite this popularity, the generation of fabric patterns remains somewhat unexplored. A possible reason for this could be that there is no publicly shared dataset large enough to train models. However, research into this topic could...

Text-to-Comic Generative Adversarial Network

Drawing and annotating comic illustrations is a complex and difficult process. No existing machine learning algorithms have been developed to create comic illustrations based on descriptions of illustrations, or the dialogue in comics. Moreover, it is not known if a generative adversarial network (GAN) can generate original comics that...

Synthesizing Comics via Conditional Generative Adversarial Networks

The creation of comic illustrations is a complex artistic process resulting in a wide variety of styles, each unique to the artist. Conditional image synthesis refers to the generation of de novo images based on certain preconditions. Applying machine learning to conditionally generate novel comics proves an intriguing yet difficult task. This...

AI for Experience: Designing with Generative Adversarial Networks to evoke climate fascination

Our world climate is an incredibly complex and ever-changing system. The reality of climate change and the consequential pressure to act fast to reduce greenhouse gas emissions requires immediate attention. And yet, despite this unprecedented urgency, active public engagement needed to motivate meaningful change is still missing.<br/> <br/> This...

Creation of new Extra-Tropical Cyclone fields in the North Atlantic with Generative Adversarial Networks: A deep learning framework to generate new synthetic atmospheric variables fields from the learned original sample data distribution

Extra-Tropical Cyclones (ETCs) are major storm system ruling and influencing the atmospheric structure at mid-latitudes. These events are usually characterized by strong winds and heavy precipitation and cause considerable storm surges with threatening wave systems for coastal regions. The possibility to simulate these storms or to increase the...

Comparing Quantitative Metrics for Generative Adversarial Neural Networks

Since their conception in 2014, a large number of Generative Adversarial Networks (GANs) [2] has been pro- posed and developed. GANs have achieved great results in realistic image generation, among other fields. Recently, stunning images have been produced. The theory and application of GANs has received much attention. However, the evaluation...

Vessel Layer Separation of X-ray Angiographic Images using Deep Learning Methods

Percutaneous coronary intervention is a minimally-invasive procedure to treat coronary artery disease. In such procedures, X-ray angiography, a real-time imaging technique, is commonly used for image guidance to identify lesion sites and navigate catheters and guide-wires within coronary arteries. Due to the physical nature of X-ray imaging,...