Add to ORKGGenerative Adversarial Networks (GAN) is a technique used to learn the distribution of some dataset in order to generate similar data. GAN models are notoriously difficult to train, which has caused limited deployment in the industry. The results of this study can be used to accelerate the process of making GANs production ready. An experiment was conducted where multiple GAN models were trained, with the hyperparameters Leaky ReLU alpha, convolutional filters, learning rate and batch size as independent variables. A Mann-Whitney U-test was used to compare the training time and training stability of each model to the others’. Except for the Leaky ReLU alpha, changes to the investigated hyperparameters had a significant effect on the trainin...
© 2019 Sukarna BaruaGenerative Adversarial Networks (GANs) are a powerful class of generative models...
Since their introduction in 2014, Generative Adversarial Networks (GAN), have been a hot topic in th...
Generative Adversarial Networks (GANs) are neural networks that allow models to learn deep represent...
Generative adversarial networks (GANs) are a class of generative models, for which the goal is to le...
Generative adversarial networks(GAN) are popular Deep learning models that can implicitly learn rich...
Generative Adversarial Networks (GANs) have been used for many applications with overwhelming succes...
In recent years, Generative Adversarial Network (GAN) and its variants have gained great popularity ...
The impressive success of Generative Adversarial Networks (GANs) is often overshadowed by the diffic...
State-of-the-art Generative Adversarial Network (GAN) often relies on stabilization methods to stabi...
GANs (generative opposing networks) are a technique for learning deep representations in the absence...
Generative Adversarial Networks (GANs) provide a novel framework and powerful tools for machine lear...
Collecting and labeling of good balanced training data are usually very difficult and challenging un...
Generating high-quality and various image samples is a significant research goal in computer vision ...
Recently generative adversarial networks are becoming the main focus area of machine learning. It wa...
In recent years, generative adversarial networks (GANs) have been proposed to generate simulated ima...
© 2019 Sukarna BaruaGenerative Adversarial Networks (GANs) are a powerful class of generative models...
Since their introduction in 2014, Generative Adversarial Networks (GAN), have been a hot topic in th...
Generative Adversarial Networks (GANs) are neural networks that allow models to learn deep represent...
Generative adversarial networks (GANs) are a class of generative models, for which the goal is to le...
Generative adversarial networks(GAN) are popular Deep learning models that can implicitly learn rich...
Generative Adversarial Networks (GANs) have been used for many applications with overwhelming succes...
In recent years, Generative Adversarial Network (GAN) and its variants have gained great popularity ...
The impressive success of Generative Adversarial Networks (GANs) is often overshadowed by the diffic...
State-of-the-art Generative Adversarial Network (GAN) often relies on stabilization methods to stabi...
GANs (generative opposing networks) are a technique for learning deep representations in the absence...
Generative Adversarial Networks (GANs) provide a novel framework and powerful tools for machine lear...
Collecting and labeling of good balanced training data are usually very difficult and challenging un...
Generating high-quality and various image samples is a significant research goal in computer vision ...
Recently generative adversarial networks are becoming the main focus area of machine learning. It wa...
In recent years, generative adversarial networks (GANs) have been proposed to generate simulated ima...
© 2019 Sukarna BaruaGenerative Adversarial Networks (GANs) are a powerful class of generative models...
Since their introduction in 2014, Generative Adversarial Networks (GAN), have been a hot topic in th...
Generative Adversarial Networks (GANs) are neural networks that allow models to learn deep represent...