Automatic Generation of Machine Learning Synthetic Data Using ROS
Naval Air Warfare Center（海军航空作战中心）
Kyle M. Hart, Ari B. Goodman, Ryan P. O'Shea
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DISTRIBUTION STATEMENT A. Approved for public release: distribution unlimited. NAWCAD-LKE Release Number 2021-72. Published in HCI International 2021 by Springer. The final authenticated version is available online at https://doi.org/10.1007/978-3-030-77772-2_21
Data labeling is a time intensive process. As such, many data scientists use various tools to aid in the data generation and labeling process. While these tools help automate labeling, many still require user interaction throughout the process. Additionally, most target only a few network frameworks. Any researchers exploring multiple frameworks must find additional tools orwrite conversion scripts. This paper presents an automated tool for generating synthetic data in arbitrary network formats. It uses Robot Operating System (ROS) and Gazebo, which are common tools in the robotics community. Through ROS paradigms, it allows extensive user customization of the simulation environment and data generation process. Additionally, a plugin-like framework allows the development of arbitrary data format writers without the need to change the main body of code. Using this tool, the authors were able to generate an arbitrarily large image dataset for three unique training formats using approximately 15 min of user setup time and a variable amount of hands-off run time, depending on the dataset size. The source code for this data generation tool is available at https://github.com/Navy-RISE-Lab/nn_data_collection
Similar to the majority of deep learning applications, diagnosing skin diseases using computer vision and deep learning often requires a large volume of data. However, obtaining sufficient data for particular types of facial skin conditions can be difficult due to privacy concerns. As a result, conditions like Rosacea are often understudied in computer-aided diagnosis. The limited availability of data for facial skin conditions has led to the investigation of alternative methods for computer-aided diagnosis. In recent years, Generative Adversarial Networks (GANs), mainly variants of StyleGANs,…
Automatic Extraction of Rules for Generating Synthetic Patient Data From Real-World Population Data Using Glioblastoma as an Example
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB（弗劳恩霍夫光学与系统技术及图像利用研究所）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所）；Bremen Cancer Registry, Leibniz Institute for Prevention Research and Epidemiology - BIPS（不莱梅癌症登记处，预防研究与流行病学研究所 - BIPS）；Institute for Pathology, Hannover Medical School（汉诺威医学院病理研究所）；Institute of Neuropathology, Medical Center - University of Freiburg（神经病理学研究所，弗赖堡大学医疗中心）；Research Data Infrastructures and Data Science, Leibniz Institute for Prevention Research and Epidemiology - BIPS（研究数据基础设施与数据科学，预防研究与流行病学研究所 - BIPS）；Department of Neuropathology, Center for Pathology, Klinikum Bremen-Mitte（神经病理学系，病理中心，不莱梅-中部诊所）；Institute of Neuropathology, RWTH Aachen University Hospital（神经病理学研究所，亚琛工业大学医院）；Robert Koch Institute, Center for Cancer Registry Data（罗伯特·科赫研究所，癌症登记数据中心）
Authentication and identification methods based on human fingerprints are ubiquitous in several systems ranging from government organizations to consumer products. The performance and reliability of such systems directly rely on the volume of data on which they have been verified. Unfortunately, a large volume of fingerprint databases is not publicly available due to many privacy and security concerns. In this paper, we introduce a new approach to automatically generate high-fidelity synthetic fingerprints at scale. Our approach relies on (i) Generative Adversarial Networks to estimate the pro…
Recent publications on automatic-speech-recognition (ASR) have a strong focus on attention encoder-decoder (AED) architectures which tend to suffer from over-fitting in low resource scenarios. One solution to tackle this issue is to generate synthetic data with a trained text-to-speech system (TTS) if additional text is available. This was successfully applied in many publications with AED systems, but only very limited in the context of other ASR architectures. We investigate the effect of varying pre-processing, the speaker embedding and input encoding of the TTS system w.r.t. the effectiven…
On the Problem of Text-To-Speech Model Selection for Synthetic Data Generation in Automatic Speech Recognition
Japan Advanced Institute of Science and Technology（日本先进科学技术学院）；RWTH Aachen University（亚琛工业大学）；AppTek GmbH（AppTek有限公司）；Nara Institute of Science and Technology（奈良科学技术学院）
Amsterdam University Medical Center, University of Amsterdam（阿姆斯特丹大学阿姆斯特丹大学医学中心）；University Medical Center Utrecht（乌得勒支大学医学中心）；Image Sciences Institute（图像科学研究所）
Accurate MR-to-CT synthesis is a requirement for MR-only workflows in radiotherapy (RT) treatment planning. In recent years, deep learning-based approaches have shown impressive results in this field. However, to prevent downstream errors in RT treatment planning, it is important that deep learning models are only applied to data for which they are trained and that generated synthetic CT (sCT) images do not contain severe errors. For this, a mechanism for online quality control should be in place. In this work, we use an ensemble of sCT generators and assess their disagreement as a measure of …
State-of-the-art stereo matching networks trained only on synthetic data often fail to generalize to more challenging real data domains. In this paper, we attempt to unfold an important factor that hinders the networks from generalizing across domains: through the lens of shortcut learning. We demonstrate that the learning of feature representations in stereo matching networks is heavily influenced by synthetic data artefacts (shortcut attributes). To mitigate this issue, we propose an Information-Theoretic Shortcut Avoidance~(ITSA) approach to automatically restrict shortcut-related informati…
Identity documents automatic reading and verification is an appealing technology for nowadays service industry, since this task is still mostly performed manually, leading to waste of economic and time resources. In this paper the prototype of a novel automatic reading system of identity documents is presented. The system has been thought to extract data of the main Italian identity documents from photographs of acceptable quality, like those usually required to online subscribers of various services. The document is first localized inside the photo, and then classified; finally, text recognit…
In this technical report I present my method for automatic synthetic dataset generation for object detection and demonstrate it on the video game League of Legends. This report furthermore serves as a handbook on how to automatically generate datasets and as an introduction on the dataset generation part of the LeagueAI framework. The LeagueAI framework is a software framework that provides detailed information about the game League of Legends based on the same input a human player would have, namely vision. The framework allows researchers and enthusiasts to develop their own intelligent agen…
Visual Car Brand Classification by Implementing a Synthetic Image Dataset Creation Pipeline
TH OWL University of Applied Sciences and Arts（TH OWL应用科学与艺术大学）；Fraunhofer IOSB-INA（弗劳恩霍夫IOSB-INA研究所）；Institute for Industrial Information Technology (inIT)（工业信息技术研究所(inIT)）
Many of the recent advances in speech separation are primarily aimed at synthetic mixtures of short audio utterances with high degrees of overlap. Most of these approaches need an additional stitching step to stitch the separated speech chunks for long form audio. Since most of the approaches involve Permutation Invariant training (PIT), the order of separated speech chunks is nondeterministic and leads to difficulty in accurately stitching homogenous speaker chunks for downstream tasks like Automatic Speech Recognition (ASR). Also, most of these models are trained with synthetic mixtures and …
The defender's perspective on automatic speaker verification: An overview
National Taiwan University（台湾大学）；The Chinese University of Hong Kong（香港中文大学）；Peoples' Friendship University of Russia (RUDN University)（俄罗斯人民友谊大学（RUDN大学））；Joint Institute for Nuclear Research（联合核研究所）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；University of Skövde（舍夫德大学）
MDM: Advancing Multi-Domain Distribution Matching for Automatic Modulation Recognition Dataset Synthesis
College of Information Engineering, Zhejiang University of Technology（浙江工业大学信息工程学院）；Institute of Cyberspace Security, Zhejiang University of Technology（浙江工业大学网络空间安全研究院）；Institute of China Electronics Technology Group Corporation 36th Research Institute（中国电子科技集团公司第三十六研究所）；College of Information And Communication Engineering, Harbin Engineering University（哈尔滨工程大学信息与通信工程学院）；National Key Laboratory of Electromagnetic Space Security（电磁空间安全全国重点实验室）
One of the challenges of using machine learning techniques with medical data is the frequent dearth of source image data on which to train. A representative example is automated lung cancer diagnosis, where nodule images need to be classified as suspicious or benign. In this work we propose an automatic synthetic lung nodule image generator. Our 3D shape generator is designed to augment the variety of 3D images. Our proposed system takes root in autoencoder techniques, and we provide extensive experimental characterization that demonstrates its ability to produce quality synthetic images.
