Generative Adversarial Networks for Bitcoin Data Augmentation

In Bitcoin entity classification, results are strongly conditioned by the ground-truth dataset, especially when applying supervised machine learning approaches. However, these ground-truth datasets are frequently affected by significant class imbalance as generally they contain much more information regarding legal services (Exchange, Gambling), than regarding services that may be related to illicit activities (Mixer, Service). Class imbalance increases the complexity of applying machine learning techniques and reduces the quality of classification results, especially for underrepresented, but critical classes. In this paper, we propose to address this problem by using Generative Adversarial Networks (GANs) for Bitcoin data augmentation as GANs recently have shown promising results in the domain of image classification. However, there is no "one-fits-all" GAN solution that works for every scenario. In fact, setting GAN training parameters is non-trivial and heavily affects the quality of the generated synthetic data. We therefore evaluate how GAN parameters such as the optimization function, the size of the dataset and the chosen batch size affect GAN implementation for one underrepresented entity class (Mining Pool) and demonstrate how a "good" GAN configuration can be obtained that achieves high similarity between synthetically generated and real Bitcoin address data. To the best of our knowledge, this is the first study presenting GANs as a valid tool for generating synthetic address data for data augmentation in Bitcoin entity classification.Comment: 8 pages, 5 figures, 4 table

Data Augmentation Using Generative Adversarial Networks

Většina dat z reálného světa není rovnoměrně rozdělena do odpovídajících tříd, ale je nevyvážená, což může mít velký vliv na kvalitu predikce klasifikačních modelů. Obecný přístup k řešení tohoto problému je modifikace původních datových sad tak, abychom dosáhli vyváženosti jednotlivých tříd. Tato práce se zaobírá vyvážením obrazových dat za pomoci generativních adversariálních sítí. Primární důraz je kladen na generování obrazových dat náležících do tříd s nedostatečným počtem reprezentantů, což je proces známý jako class balancing. Práce se zabývá analýzou a porovnáním různých technik používaných pro rozšíření dat, jako jsou geometrické metody nebo modely založené na principu neuronových sítí. Vyhodnocení je provedeno pomocí klasifikačních modelů, natrénovaných na původních, nevyvážených i uměle vyvážených datových sadách. Dosažené výsledky naznačují, jak schopnost jednotlivých metod rozšířit datové sady klesá se zvětšující mírou nevyvážení a rozmanitostí těchto sad.Most labelled real-world data is not uniformly distributed within classes, which can have a severe impact on the prediction quality of classification models. A general approach is to overcome this issue by modifying the original data to restore the balance of the classes. This thesis deals with balancing image datasets by data augmentation using generative adversarial neural networks. The primary focus is on generating images of underrepresented classes in imbalanced datasets, which is a process known as class balancing. The aim of this thesis is to analyse and compare data augmentation techniques including standard methods, generative adversarial networks and autoencoders. Evaluation is done using classifiers trained on the original, unbalanced and augmented datasets. The results achieved suggest how the performance of the methods proportionately deteriorates with increasing imbalance rate and diversity of datasets

Generative Adversarial Networks for Data Augmentation

One way to expand the available dataset for training AI models in the medical field is through the use of Generative Adversarial Networks (GANs) for data augmentation. GANs work by employing a generator network to create new data samples that are then assessed by a discriminator network to determine their similarity to real samples. The discriminator network is taught to differentiate between actual and synthetic samples, while the generator system is trained to generate data that closely resemble real ones. The process is repeated until the generator network can produce synthetic data that is indistinguishable from genuine data. GANs have been utilized in medical image analysis for various tasks, including data augmentation, image creation, and domain adaptation. They can generate synthetic samples that can be used to increase the available dataset, especially in cases where obtaining large amounts of genuine data is difficult or unethical. However, it is essential to note that the use of GANs in medical imaging is still an active area of research to ensure that the produced images are of high quality and suitable for use in clinical settings.Comment: 13 pages, 6 figures, 1 table; Acceptance of the chapter for the Springer book "Data-driven approaches to medical imaging

GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks

One of the biggest issues facing the use of machine learning in medical imaging is the lack of availability of large, labelled datasets. The annotation of medical images is not only expensive and time consuming but also highly dependent on the availability of expert observers. The limited amount of training data can inhibit the performance of supervised machine learning algorithms which often need very large quantities of data on which to train to avoid overfitting. So far, much effort has been directed at extracting as much information as possible from what data is available. Generative Adversarial Networks (GANs) offer a novel way to unlock additional information from a dataset by generating synthetic samples with the appearance of real images. This paper demonstrates the feasibility of introducing GAN derived synthetic data to the training datasets in two brain segmentation tasks, leading to improvements in Dice Similarity Coefficient (DSC) of between 1 and 5 percentage points under different conditions, with the strongest effects seen fewer than ten training image stacks are available