Add to ORKGThis paper investigates data synthesis with a Generative Adversarial Network (GAN) for augmenting the amount of data used for training classifiers (in supervised learning) to compensate for class imbalance (when the classes are not represented equally by the same number of training samples). Our data synthesis approach with GAN is compared with data augmentation in the context of image classification. Our experimental results show encouraging results in comparison to standard data augmentation schemes based on image transforms
Some downstream tasks often require enough data for training in deep learning, but it is formidable ...
Class imbalance is one of the most basic and important problems of web data. The key to overcoming t...
Generative adversarial networks (GAN) have attracted significant attention from the research communi...
This paper investigates data synthesis with a Generative Adversarial Network (GAN) for augmenting th...
This paper investigates data synthesis with a Generative Adversarial Network (GAN) for augmenting th...
Deep learning has achieved significant improvements in a variety of tasks in computer vision applica...
Class-imbalanced datasets are common across different domains such as health, banking, security and ...
Generative Adversarial Networks (GANs) provide a novel framework and powerful tools for machine lear...
Generative Adversarial Networks (GANs) have been extremely successful in various application domains...
Generative Adversarial Networks (GANs) provide a novel framework and powerful tools for machine lear...
Generative Adversarial Networks (GANs) have been extremely successful in various application domains...
Generative Adversarial Networks (GANs) have been extremely successful in various application domains...
Generative Adversarial Networks (GANs) have been extremely successful in various application domains...
Any computer vision application development starts off by acquiring images and data, then preprocess...
Class-imbalanced datasets often contain one or more class that are under-represented in a dataset. I...
Some downstream tasks often require enough data for training in deep learning, but it is formidable ...
Class imbalance is one of the most basic and important problems of web data. The key to overcoming t...
Generative adversarial networks (GAN) have attracted significant attention from the research communi...
This paper investigates data synthesis with a Generative Adversarial Network (GAN) for augmenting th...
This paper investigates data synthesis with a Generative Adversarial Network (GAN) for augmenting th...
Deep learning has achieved significant improvements in a variety of tasks in computer vision applica...
Class-imbalanced datasets are common across different domains such as health, banking, security and ...
Generative Adversarial Networks (GANs) provide a novel framework and powerful tools for machine lear...
Generative Adversarial Networks (GANs) have been extremely successful in various application domains...
Generative Adversarial Networks (GANs) provide a novel framework and powerful tools for machine lear...
Generative Adversarial Networks (GANs) have been extremely successful in various application domains...
Generative Adversarial Networks (GANs) have been extremely successful in various application domains...
Generative Adversarial Networks (GANs) have been extremely successful in various application domains...
Any computer vision application development starts off by acquiring images and data, then preprocess...
Class-imbalanced datasets often contain one or more class that are under-represented in a dataset. I...
Some downstream tasks often require enough data for training in deep learning, but it is formidable ...
Class imbalance is one of the most basic and important problems of web data. The key to overcoming t...
Generative adversarial networks (GAN) have attracted significant attention from the research communi...