scholarly journals Autoencoder-Combined Generative Adversarial Networks for Synthetic Image Data Generation and Detection of Jellyfish Swarm

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 54207-54214 ◽  
Author(s):  
Kyukwang Kim ◽  
Hyun Myung
Keyword(s):  
Image Data ◽  
Generative Adversarial Networks ◽  
Synthetic Image ◽  
Data Generation ◽  
Adversarial Networks

scholarly journals A Survey on Generative Adversarial Networks for imbalance problems in computer vision tasks

2020 ◽  
Author(s):  
Vignesh Sampath ◽  
Iñaki Maurtua ◽  
Juan José Aguilar Martín ◽  
Aitor Gutierrez
Keyword(s):  
Computer Vision ◽  
Real World ◽  
Medical Image Analysis ◽  
Image Data ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Metallic Surface ◽  
Synthetic Image ◽  
Adversarial Networks ◽  
Model Complex

Abstract Any computer vision application development starts off by acquiring images and data, then preprocessing and pattern recognition steps to perform a task. When the acquired images are highly imbalanced and not adequate, the desired task may not be achievable. Unfortunately, the occurrence of imbalance problems in acquired image datasets in certain complex real-world problems such as anomaly detection, emotion recognition, medical image analysis, fraud detection, metallic surface defect detection, disaster prediction, etc., are inevitable. The performance of computer vision algorithms can significantly deteriorate when the training dataset is imbalanced. In recent years, Generative Adversarial Networks (GANs) have gained immense attention by researchers across a variety of application domains due to their capability to model complex real-world image data. It is particularly important that GANs can not only be used to generate synthetic images, but also its fascinating adversarial learning idea showed good potential in restoring balance in imbalanced datasets.In this paper, we examine the most recent developments of GANs based techniques for addressing imbalance problems in image data. The real-world challenges and implementations of synthetic image generation based on GANs are extensively covered in this survey. Our survey first introduces various imbalance problems in computer vision tasks and its existing solutions, and then examines key concepts such as deep generative image models and GANs. After that, we propose a taxonomy to summarize GANs based techniques for addressing imbalance problems in computer vision tasks into three major categories: 1. Image level imbalances in classification, 2. object level imbalances in object detection and 3. pixel level imbalances in segmentation tasks. We elaborate the imbalance problems of each group, and provide GANs based solutions in each group. Readers will understand how GANs based techniques can handle the problem of imbalances and boost performance of the computer vision algorithms.

scholarly journals A Survey on Generative Adversarial Networks for imbalance problems in computer vision tasks

2020 ◽  
Author(s):  
Vignesh Sampath ◽  
Iñaki Maurtua ◽  
Juan José Aguilar Martín ◽  
Aitor Gutierrez
Keyword(s):  
Computer Vision ◽  
Real World ◽  
Medical Image Analysis ◽  
Image Data ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Metallic Surface ◽  
Synthetic Image ◽  
Adversarial Networks ◽  
Model Complex

Abstract Any computer vision application development starts off by acquiring images and data, then preprocessing and pattern recognition steps to perform a task. When the acquired images are highly imbalanced and not adequate, the desired task may not be achievable. Unfortunately, the occurrence of imbalance problems in acquired image datasets in certain complex real-world problems such as anomaly detection, emotion recognition, medical image analysis, fraud detection, metallic surface defect detection, disaster prediction, etc., are inevitable. The performance of computer vision algorithms can significantly deteriorate when the training dataset is imbalanced. In recent years, Generative Adversarial Networks (GANs) have gained immense attention by researchers across a variety of application domains due to their capability to model complex real-world image data. It is particularly important that GANs can not only be used to generate synthetic images, but also its fascinating adversarial learning idea showed good potential in restoring balance in imbalanced datasets. In this paper, we examine the most recent developments of GANs based techniques for addressing imbalance problems in image data. The real-world challenges and implementations of synthetic image generation based on GANs are extensively covered in this survey. Our survey first introduces various imbalance problems in computer vision tasks and its existing solutions, and then examine key concepts such as deep generative image models and GANs. After that, we propose a taxonomy to summarize GANs based techniques for addressing imbalance problems in computer vision tasks into three major categories: 1. Image level imbalances in classification, 2. object level imbalances in object detection and 3. pixel level imbalances in segmentation tasks. We elaborate the imbalance problems of each group, and further provide GANs based solutions in each group. Readers will understand how GANs based techniques can handle the problem of imbalances and boost performance of the computer vision algorithms.

Intrusion detection of railway clearance from infrared images using generative adversarial networks

2020 ◽  
pp. 1-13
Author(s):  
Yundong Li ◽  
Yi Liu ◽  
Han Dong ◽  
Wei Hu ◽  
Chen Lin
Keyword(s):  
Intrusion Detection ◽  
Synthetic Data ◽  
Generative Adversarial Networks ◽  
Generation Model ◽  
Single Shot ◽  
Data Generation ◽  
Infrared Images ◽  
Adversarial Networks ◽  
Training Samples ◽  
Rgb Images

The intrusion detection of railway clearance is crucial for avoiding railway accidents caused by the invasion of abnormal objects, such as pedestrians, falling rocks, and animals. However, detecting intrusions using deep learning methods from infrared images captured at night remains a challenging task because of the lack of sufficient training samples. To address this issue, a transfer strategy that migrates daytime RGB images to the nighttime style of infrared images is proposed in this study. The proposed method consists of two stages. In the first stage, a data generation model is trained on the basis of generative adversarial networks using RGB images and a small number of infrared images, and then, synthetic samples are generated using a well-trained model. In the second stage, a single shot multibox detector (SSD) model is trained using synthetic data and utilized to detect abnormal objects from infrared images at nighttime. To validate the effectiveness of the proposed method, two groups of experiments, namely, railway and non-railway scenes, are conducted. Experimental results demonstrate the effectiveness of the proposed method, and an improvement of 17.8% is achieved for object detection at nighttime.

scholarly journals InvNet: Encoding Geometric and Statistical Invariances in Deep Generative Models

2020 ◽  
Vol 34 (04) ◽  
pp. 4377-4384
Author(s):  
Ameya Joshi ◽  
Minsu Cho ◽  
Viraj Shah ◽  
Balaji Pokuri ◽  
Soumik Sarkar ◽  
...  
Keyword(s):  
Image Data ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Complex Data ◽  
Challenging Problem ◽  
Two Phase ◽  
Adversarial Networks ◽  
Generative Modeling ◽  
Lack Of Control ◽  
Real World Problems

Generative Adversarial Networks (GANs), while widely successful in modeling complex data distributions, have not yet been sufficiently leveraged in scientific computing and design. Reasons for this include the lack of flexibility of GANs to represent discrete-valued image data, as well as the lack of control over physical properties of generated samples. We propose a new conditional generative modeling approach (InvNet) that efficiently enables modeling discrete-valued images, while allowing control over their parameterized geometric and statistical properties. We evaluate our approach on several synthetic and real world problems: navigating manifolds of geometric shapes with desired sizes; generation of binary two-phase materials; and the (challenging) problem of generating multi-orientation polycrystalline microstructures.

scholarly journals Blind Image Separation Method Based on Cascade Generative Adversarial Networks

2021 ◽  
Vol 11 (20) ◽  
pp. 9416
Author(s):  
Fei Jia ◽  
Jindong Xu ◽  
Xiao Sun ◽  
Yongli Ma ◽  
Mengying Ni
Keyword(s):  
Prior Knowledge ◽  
Image Synthesis ◽  
Generative Adversarial Networks ◽  
Synthetic Image ◽  
Adversarial Networks ◽  
Training Samples ◽  
Image Separation ◽  
Blind Image ◽  
Insufficient Number ◽  
Image Datasets

To solve the challenge of single-channel blind image separation (BIS) caused by unknown prior knowledge during the separation process, we propose a BIS method based on cascaded generative adversarial networks (GANs). To ensure that the proposed method can perform well in different scenarios and to address the problem of an insufficient number of training samples, a synthetic network is added to the separation network. This method is composed of two GANs: a U-shaped GAN (UGAN), which is used to learn image synthesis, and a pixel-to-attention GAN (PAGAN), which is used to learn image separation. The two networks jointly complete the task of image separation. UGAN uses the unpaired mixed image and the unmixed image to learn the mixing style, thereby generating an image with the “true” mixing characteristics which addresses the problem of an insufficient number of training samples for the PAGAN. A self-attention mechanism is added to the PAGAN to quickly extract important features from the image data. The experimental results show that the proposed method achieves good results on both synthetic image datasets and real remote sensing image datasets. Moreover, it can be used for image separation in different scenarios which lack prior knowledge and training samples.

scholarly journals Cross Data Set Generalization of Ultrasound Image Augmentation using Representation Learning: A Case Study

2021 ◽  
Vol 7 (2) ◽  
pp. 755-758
Author(s):  
Daniel Wulff ◽  
Mohamad Mehdi ◽  
Floris Ernst ◽  
Jannis Hagenah
Keyword(s):  
Data Augmentation ◽  
Ultrasound Image ◽  
Image Data ◽  
Representation Learning ◽  
Generative Adversarial Networks ◽  
Data Sets ◽  
Imaging Data ◽  
Data Set ◽  
Adversarial Networks ◽  
Classical Image

Abstract Data augmentation is a common method to make deep learning assessible on limited data sets. However, classical image augmentation methods result in highly unrealistic images on ultrasound data. Another approach is to utilize learning-based augmentation methods, e.g. based on variational autoencoders or generative adversarial networks. However, a large amount of data is necessary to train these models, which is typically not available in scenarios where data augmentation is needed. One solution for this problem could be a transfer of augmentation models between different medical imaging data sets. In this work, we present a qualitative study of the cross data set generalization performance of different learning-based augmentation methods for ultrasound image data. We could show that knowledge transfer is possible in ultrasound image augmentation and that the augmentation partially results in semantically meaningful transfers of structures, e.g. vessels, across domains.

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