Deep Learning for Imbalance Data Classification using Class Expert Generative Adversarial Network

Fanny, .; Cenggoro, Tjeng Wawan

doi:10.1016/j.procs.2018.08.150

Cited by 49 publications

(12 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On FMNIST [202] dataset, performance improved from 91.9% accuracy and 0.921 F1-score using augmentation to 92.8% accuracy and 0.923 F1score using WGAN-GP. An idea of GANs based transfer learning technique for multiclass imbalance problem is proposed by Fanny et al [203]. Their architecture named class expert generative adversarial network (CE-GAN) makes use of multiple GANs models, a separate GANs for each class.…”

Section: Multiclass Imbalancementioning

confidence: 99%

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

Sampath

Maurtua

Martín

et al. 2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Multiclass Imbalancementioning

confidence: 99%

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

Sampath

Maurtua

Martín

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…On FMNIST [204] dataset, performance improved from 91.9% accuracy and 0.921 F1-score using augmentation to 92.8% accuracy and 0.923 F1score using WGAN-GP. An idea of GANs based transfer learning technique for multiclass imbalance problem is proposed by Fanny et al [205]. Their architecture named class expert generative adversarial network (CE-GAN) makes use of multiple GANs models, a separate GANs for each class.…”

Section: Multiclass Imbalancementioning

confidence: 99%

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

Sampath

Maurtua

Martín

et al. 2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…In fact, imbalanced classes exist widely in the practical world, such as classification of junk mails, medical diagnosis and advertisement recommendation events [13][14][15]. Traditional classification methods are easy to produce deviation for multiple sample categories in imbalanced classification tasks, thus resulting in low accuracy.…”

Section: A Sample Supplementationmentioning

confidence: 99%

Research on Imbalanced Microscopic Image Classification of Harmful Algae

Qiao

2020

IEEE Access

View full text Add to dashboard Cite

Image analysis based on biological morphological differences is an important development direction for classification and determination of planktonic algae. However, it has some shortages, such as high degree of sample imbalance and difficult to have formalized description of local physiological features. To overcome these shortages, this study decomposed recognition of harmful algae microscopic images into sample supplementation, accurate segmentation, feature extraction and classification and identification. Firstly, sample imbalance is solved by Kernel-ADASYN method to generate enough samples. Target cells are separated through integration of multi-directional projections. Refined segmentation between the spine and cingulum detail regions is further realized. Later, effective feature extraction and description of global and local features were performed one by one by matching physiological features of algae with machine recognition features. Finally, the SVM model was applied for multi-class recognition. Results demonstrated that the proposed method can reduce imbalance rate of sample size and realize multi-class recognition of microscopic images of 15 categories of algae cells. INDEX TERMS Imbalanced classification, microscopic image recognition, multi-level features extraction LIST OF ABBREVIATIONS Kernel-ADASYN Kernel based adaptive synthetic CCD Charge coupled device GLCM Gray-level co-occurrence matrix ASM Angular second moment SVM Support vector machine PCA Principal component analysis RBF Radial base function

show abstract

Deep Learning for Imbalance Data Classification using Class Expert Generative Adversarial Network

Cited by 49 publications

References 11 publications

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

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

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

Research on Imbalanced Microscopic Image Classification of Harmful Algae

Contact Info

Product

Resources

About