Deep Convolutional Neuronal Networks (DCNNs) are showing remarkable performance on many computer vision tasks. Due to their large parameter space, they require many labeled samples when trained in a supervised setting. The costs of annotating data manually can render the use of DCNNs infeasible. We present a novel framework called RenderGAN that can generate large amounts of realistic, labeled images by combining a 3D model and the Generative Adversarial Network framework. In our approach, image augmentations (e.g. lighting, background, and detail) are learned from unlabeled data such that the generated images are strikingly realistic while preserving the labels known from the 3D model. We apply the RenderGAN framework to generate images of barcode-like markers that are attached to honeybees. Training a DCNN on data generated by the RenderGAN yields considerably better performance than training it on various baselines.
Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation
National Center for Tumor Diseases（国家肿瘤疾病中心）；University College London（伦敦大学学院）；Mannheim University of Applied Sciences（曼海姆应用科学大学）；German Cancer Research Center（德国癌症研究中心）
Semi-supervised Learning for Few-shot Image-to-Image Translation
Computer Vision Center, Universitat Autònoma de Barcelona（巴塞罗那自治大学计算机视觉中心）；Inception Institute of Artificial Intelligence（人工智能起源研究所）；Linköping University（林雪平大学）
KTH Royal Institute of Technology（瑞典皇家理工学院）；Silo AI（赛罗人工智能公司）
Autonomous agents, such as driverless cars, require large amounts of labeled visual data for their training. A viable approach for acquiring such data is training a generative model with collected real data, and then augmenting the collected real dataset with synthetic images from the model, generated with control of the scene layout and ground truth labeling. In this paper we propose Full-Glow, a fully conditional Glow-based architecture for generating plausible and realistic images of novel street scenes given a semantic segmentation map indicating the scene layout. Benchmark comparisons sho…
Limited availability of labeled-data makes any supervised learning problem challenging. Alternative learning settings like semi-supervised and universum learning alleviate the dependency on labeled data, but still require a large amount of unlabeled data, which may be unavailable or expensive to acquire. GAN-based data generation methods have recently shown promise by generating synthetic samples to improve learning. However, most existing GAN based approaches either provide poor discriminator performance under limited labeled data settings; or results in low quality generated data. In this pa…
A Data Augmentation Pipeline to Generate Synthetic Labeled Datasets of 3D Echocardiography Images using a GAN
GE Vingmed Ultrasound - GE Healthcare（GE Vingmed超声 - GE医疗）；CARIM School for Cardiovascular Diseases（CARIM心血管疾病学院）；Maastricht University Medical Center（马斯特里赫特大学医学中心）
Universidad Nacional de Colombia（哥伦比亚国立大学）；King Fahd University of Petroleum and Minerals（法赫德国王石油与矿业大学）
Deep learning architectures have enriched data analytics in the geosciences, complementing traditional approaches to geological problems. Although deep learning applications in geosciences show encouraging signs, the actual potential remains untapped. This is primarily because geological datasets, particularly petrography, are limited, time-consuming, and expensive to obtain, requiring in-depth knowledge to provide a high-quality labeled dataset. We approached these issues by developing a novel deep learning framework based on generative adversarial networks (GANs) to create the first realisti…
Label-efficient multi-organ segmentation with a diffusion model
College of Big Data and Internet, Shenzhen Technology University（深圳技术大学大数据与互联网学院）；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；Department of Radiology, Beijing Tiantan Hospital, Capital Medical University（首都医科大学附属北京天坛医院放射科）
Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT
a Division of Medical Informatics, Department of Biomedical Engineering, Link\" o ping University, Link\" o ping, Sweden b Division of Statistics；Machine Learning, Department of Computer；Information Science, Link\" o ping University, Link\" o ping, Sweden c Center for Medical Image Science；Visualization (CMIV), Link\" o ping University, Link\" o ping, Sweden
On the effectiveness of GAN generated cardiac MRIs for segmentation
Université de Bourgogne Franche-Comté（勃艮第-弗朗什-孔泰大学）；Cardiac Simulation & Imaging Software（心脏模拟与成像软件公司）；University of Sherbrooke（舍布鲁克大学）；University Hospital of Dijon（第戎大学医院）
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University（海德堡大学）；Helmholtz Information and Data Science School for Health（亥姆霍兹健康信息与数据科学学院）；Cancer Research UK Cambridge Institute, University of Cambridge（剑桥大学英国癌症研究中心剑桥研究所）
Photoacoustic tomography (PAT) has the potential to recover morphological and functional tissue properties with high spatial resolution. However, previous attempts to solve the optical inverse problem with supervised machine learning were hampered by the absence of labeled reference data. While this bottleneck has been tackled by simulating training data, the domain gap between real and simulated images remains an unsolved challenge. We propose a novel approach to PAT image synthesis that involves subdividing the challenge of generating plausible simulations into two disjoint problems: (1) Pro…
