18,983 research outputs found
Synthesising Tabular Datasets Using Wasserstein Conditional GANS with Gradient Penalty (WCGAN-GP)
Deep learning based methods based on Generative Adversarial Networks (GANs) have seen remarkable success in data synthesis of images and text. This study investigates the use of GANs for the generation of tabular mixed dataset. We apply Wasserstein Conditional Generative Adversarial Network (WCGAN-GP) to the task of generating tabular synthetic data that is indistinguishable from the real data, without incurring information leakage. The performance of WCGAN-GP is compared against both the ground truth datasets and SMOTE using three labelled real-world datasets from different domains. Our results for WCGAN-GP show that the synthetic data preserves distributions and relationships of the real data, outperforming the SMOTE approach on both class preservation and data protection metrics. Our work is a contribution towards the automated synthesis of tabular mixed dat
Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)
Positron emission tomography (PET) image synthesis plays an important role,
which can be used to boost the training data for computer aided diagnosis
systems. However, existing image synthesis methods have problems in
synthesizing the low resolution PET images. To address these limitations, we
propose multi-channel generative adversarial networks (M-GAN) based PET image
synthesis method. Different to the existing methods which rely on using
low-level features, the proposed M-GAN is capable to represent the features in
a high-level of semantic based on the adversarial learning concept. In
addition, M-GAN enables to take the input from the annotation (label) to
synthesize the high uptake regions e.g., tumors and from the computed
tomography (CT) images to constrain the appearance consistency and output the
synthetic PET images directly. Our results on 50 lung cancer PET-CT studies
indicate that our method was much closer to the real PET images when compared
with the existing methods.Comment: 9 pages, 2 figure
Unsupervised Text Embedding Space Generation Using Generative Adversarial Networks for Text Synthesis
Generative Adversarial Networks (GAN) is a model for data synthesis, which
creates plausible data through the competition of generator and discriminator.
Although GAN application to image synthesis is extensively studied, it has
inherent limitations to natural language generation. Because natural language
is composed of discrete tokens, a generator has difficulty updating its
gradient through backpropagation; therefore, most text-GAN studies generate
sentences starting with a random token based on a reward system. Thus, the
generators of previous studies are pre-trained in an autoregressive way before
adversarial training, causing data memorization that synthesized sentences
reproduce the training data. In this paper, we synthesize sentences using a
framework similar to the original GAN. More specifically, we propose Text
Embedding Space Generative Adversarial Networks (TESGAN) which generate
continuous text embedding spaces instead of discrete tokens to solve the
gradient backpropagation problem. Furthermore, TESGAN conducts unsupervised
learning which does not directly refer to the text of the training data to
overcome the data memorization issue. By adopting this novel method, TESGAN can
synthesize new sentences, showing the potential of unsupervised learning for
text synthesis. We expect to see extended research combining Large Language
Models with a new perspective of viewing text as an continuous space
- …