26 papers · ranked by Valyu relevance
Yuanming Li, Bonhwa Ku, Shou Zhang, Jae-Kwang Ahn + 1 more
Realistic synthetic data can be useful for data augmentation when training deep learning models to improve seismological detection and classification performance. In recent years, various deep learning techniques have been successfully applied in modern seismology. Due to the performance of deep learning depends on a…
Ibrahim Alsaggaf, Daniel Buchan, Cen Wan
Cell-type identification plays a fundamental role in single-cell RNA-Seq analytics. Thanks to the recent success of the contrastive learning paradigm, the accuracy of automatic cell-type identification has also been improved. In this work, we propose a novel contrastive learning-based cell-type identification method…
Matthew Amodio, Dennis Shung, Daniel Burkhardt, Patrick Wong + 6 more
In many important contexts involving measurements of biological entities, there are distinct categories of information: some information is easy-to-obtain information (EI) and can be gathered on virtually every subject of interest, while other information is hard-to-obtain information (HI) and can only be gathered on…
Liang Hou, Qi Cao, Huawei Shen, Xueqi Cheng
Conditional generative models aim to learn the underlying joint distribution of data and labels to achieve conditional data generation. Among them, the auxiliary classifier generative adversarial network (AC-GAN) has been widely used, but suffers from the problem of low intra-class diversity of the generated samples.…
Haoyu Lan, Arthur W Toga, Farshid Sepehrband
Image synthesis is one of the key applications of deep learning in neuroimaging, which enables shortening of the scan time and/or improve image quality; therefore, reducing the imaging cost and improving patient experience. Given the multi-modal and large-scale nature of neuroimaging data, the synthesis task is…
Soheil Saghafi, Timothy Rumbell, Viatcheslav Gurev, James Kozloski + 3 more
'Francesco Tamagnini' 'Kyle C. A. Wedgwood' 'Casey O. Diekman'] Alzheimer’s disease (AD) is believed to occur when abnormal amounts of the proteins amyloid beta and tau aggregate in the brain, resulting in a progressive loss of neuronal function. Hippocampal neurons in transgenic mice with amyloidopathy or tauopathy…
Houssem Eddine Boulahbal, Adrian Voicila, Andrew I. Comport
This paper proposes two important contributions for conditional Generative Adversarial Networks (cGANs) to improve the wide variety of applications that exploit this architecture. The first main contribution is an analysis of cGANs to show that they are not explicitly conditional. In particular, it will be shown that…
Tuan Dinh, Daewon Seo, Zhixu Du, Shangdong Liang + 1 more
We study the GAN conditioning problem, whose goal is to convert a pretrained unconditional GAN into a conditional GAN using labeled data. We first identify and analyze three approaches to this problem – conditional GAN training from scratch, fine-tuning, and input reprogramming. Our analysis reveals that when the…
Adeel Mufti, Biagio Antonelli, Julius Monello
We examined the use of modern Generative Adversarial Nets to generate novel images of oil paintings using the Painter By Numbers dataset. We implemented Spectral Normalization GAN (SN-GAN) and Spectral Normalization GAN with Gradient Penalty, and compared their outputs to a Deep Convolutional GAN. Visually, and…
Sihao Ding, Andreas Wallin
A conditional Generative Adversarial Network allows for generating samples conditioned on certain external information. Being able to recover latent and conditional vectors from a conditional GAN can be potentially valuable in various applications, ranging from image manipulation for entertaining purposes to diagnosis…
Shaked Ahronoviz, Ilan Gronau
In recent years, there have been increasing attempts to develop computational methods for generating synthetic genomic data that aim to mimic real genomic datasets. Artificial genomes (AGs) generated by these methods have emerged as a promising potential solution for privacy concerns raised by public genomic datasets…
Anis Bourou, Valérie Mezger, Auguste Genovesio
In recent years, Generative Adversarial Networks (GANs) have seen significant advancements, leading to their widespread adoption across various fields. The original GAN architecture enables the generation of images without any specific control over the content, making it an unconditional generation process. However…
Shih-Kai Hung, John Q. Gan, Kathiravan Srinivasan
Image data collection and labelling is costly or difficult in many real applications. Generating diverse and controllable images using conditional generative adversarial networks (GANs) for data augmentation from a small dataset is promising but challenging as deep convolutional neural networks need a large training…
Samah S. Baraheem, Tam V. Nguyen, Cosimo Distante
The proliferation of Artificial Intelligence (AI) models such as Generative Adversarial Networks (GANs) has shown impressive success in image synthesis. Artificial GAN-based synthesized images have been widely spread over the Internet with the advancement in generating naturalistic and photo-realistic images. This…
Alejandro González, Manel Mateos, Felipe Perez-Stoppa, Ester Vidaña-Vila + 2 more
'Ester Vidaña-Vila' 'Joan Navarro' 'Xavier Sevillano'] Generative Adversarial Networks (GANs) are an arrange of two neural networks –the generator and the discriminator– that are jointly trained to generate artificial data, such as images, from random inputs. The quality of these generated images has recently reached…
Tim Kucera, Matteo Togninalli, Laetitia Meng-Papaxanthos
Protein Design has become increasingly important for medical and biotechnological applications. Because of the complex mechanisms underlying protein formation, the creation of a novel protein requires tedious and time-consuming computational or experimental protocols. At the same time, Machine Learning has enabled to…
Hengshi Yu, Joshua D. Welch
Deep generative models, including variational autoencoders (VAEs) and generative adversarial networks (GANs), have achieved remarkable successes in generating and manipulating highdimensional images. VAEs excel at learning disentangled image representations, while GANs excel at generating realistic images. Here, we…
Lianchao Jin, Fuxiao Tan, Shengming Jiang
Computer vision is one of the hottest research fields in deep learning. The emergence of generative adversarial networks (GANs) provides a new method and model for computer vision. The idea of GANs using the game training method is superior to traditional machine learning algorithms in terms of feature learning and…
Dani Kiyasseh, Girmaw Abebe Tadesse, Le Nguyen Thanh Nhan, Le Van Tan + 3 more
The paucity of physiological time-series data collected from low-resource clinical settings limits the capabilities of modern machine learning algorithms in achieving high performance. Such performance is further hindered by class imbalance; datasets where a diagnosis is much more common than others. To overcome these…
Alex Ling Yu Hung, Kai Zhao, Haoxin Zheng, Ran Yan + 7 more
Conditional image generation plays a vital role in medical image analysis as it is effective in tasks such as super-resolution, denoising, and inpainting, among others. Diffusion models have been shown to perform at a state-of-the-art level in natural image generation, but they have not been thoroughly studied in…
Jens Nußberger, Frederic Boesel, Stefan Lenz, Harald Binder + 1 more
Deep generative models can be trained to represent the joint distribution of data, such as measurements of single nucleotide polymorphisms (SNPs) from several individuals. Subsequently, synthetic observations are obtained by drawing from this distribution. This has been shown to be useful for several tasks, such as…
Nathan Mancheun Lui, Max D Li, Matthew Ford
Deep generative models for molecular graphs offer a new avenue for property optimization in drug discovery. Optimizing differentiable models that generate molecular graphs is certainly faster, cheaper, and much more accessible than traditional methods of chemical synthesis. Recent advances in generative modeling have…
Authors not listed
Solving optimization problems, especially for nonlinear and constrained systems, is a challenge. Decades of specialized algorithms have been developed for general and special cases of root finding, minimization (including constraints), for parameter estimation, and mapping connected spaces. These approaches typically…
Jie Lin, Mingyuan Xu, Hongming Chen
Shape-based virtual screening is a widely utilized method in ligand-based de novo drug design, aiming to identify molecules in chemical libraries that share similar 3D shapes but simultaneously possess novel 2D chemical structures compared to the reference compound. As an emerging technology, generative model is an…
Authors not listed
Three-dimensional molecular generative models have emerged that produce de novo molecules both unconditionally and conditionally, e.g., within protein pockets. However, steering those models in a specific region of the chemical space that satisfies a set of desired properties remains challenging. In this study, we…
Junkil Park, Aseem Partap Singh Gill, Seyed Mohamad Moosavi, JIHAN KIM
The success of diffusion models in the field of image processing has propelled the creation of software such as Dall-E, Midjourney and Stable Diffusion, which are tools used for text-to-image generations. Mapping this workflow onto materials discovery, a new diffusion model was developed for the generation of pure…