LogIn / RegistrationArticles for sale
- Head office address: Moscow, 123317, Moscow City, 8th Floor, Presnenskaya Embankment, 6, bldg. 2
- article@123mi.ru
- Сортировка:
- по горящим
- по цене
- по дате
#6384. Multi-class imbalanced image classification using conditioned GANs
| October 2026 | publication date |
| Proposal available till | 22-05-2025 |
| 4 total number of authors per manuscript | 0 $ |
| The title of the journal is available only for the authors who have already paid for |
| Journal’s subject area: |
| Library and Information Sciences; Media Technology; Information Systems; |
| place 1 | place 2 | place 3 | place 4 |
| Free | Free | Free | Free |
| 2350 $ | 1200 $ | 1050 $ | 900 $ |
Contract №6384.1   | Contract №6384.2 | Contract №6384.3 | Contract №6384.4 |
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)
Abstract:
The problem of data skewness, class imbalance, data scarcity and noise limits the application of machine learning and deep learning models in applications like anomaly detection, fraud detection, intrusion detection, fault diagnosis, machine-to-machine communication, etc. Performance of supervised learning leans towards majority class and fails to generalize testing data in class imbalance and noisy data problems. Using neural-based data augmentation techniques for data generation and deep convolutional models for classification would enhance the performance of the applications mentioned above. Recently, GANs (generative adversarial networks) showed significant improvements in generating images. In this paper, a model that uses a conditioned deep convolutional GAN and an auxiliary classifier are proposed to tackle the aforementioned issues. The conditioned GAN is used for data generation of minority classes images and noisy images. Another auxiliary deep convolutional model is employed for the classification of images on data augmented dataset. Also, a multi-hinge loss is employed in both the data augmentation and classification tasks. The effectiveness of the proposed model is investigated on the quality of generated images and classification metrics using four publicly available popular datasets: MNIST, EMNIST, CIFAR10, and SVHN.
Keywords:
Adversarial network; Convolutional neural network; Data augmentation; Generative model; Imbalanced data
Contacts :
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)
Abstract:
The problem of data skewness, class imbalance, data scarcity and noise limits the application of machine learning and deep learning models in applications like anomaly detection, fraud detection, intrusion detection, fault diagnosis, machine-to-machine communication, etc. Performance of supervised learning leans towards majority class and fails to generalize testing data in class imbalance and noisy data problems. Using neural-based data augmentation techniques for data generation and deep convolutional models for classification would enhance the performance of the applications mentioned above. Recently, GANs (generative adversarial networks) showed significant improvements in generating images. In this paper, a model that uses a conditioned deep convolutional GAN and an auxiliary classifier are proposed to tackle the aforementioned issues. The conditioned GAN is used for data generation of minority classes images and noisy images. Another auxiliary deep convolutional model is employed for the classification of images on data augmented dataset. Also, a multi-hinge loss is employed in both the data augmentation and classification tasks. The effectiveness of the proposed model is investigated on the quality of generated images and classification metrics using four publicly available popular datasets: MNIST, EMNIST, CIFAR10, and SVHN.
Keywords:
Adversarial network; Convolutional neural network; Data augmentation; Generative model; Imbalanced data
Contacts :
