Add to ORKGGenerative Adversarial Networks (GANs) brought rapid developments in generating synthetic images by mimicking structures in the training data. With the list of application of GANs growing drastically, it has lately become an exciting technology to explore for designers to communicate their ideas and arts through technology and create engaging experiences for humans. Nevertheless, translating human experiences to artificial intelligence and creating visually pleasant imagery is a challenging task due to complex semantics of human perception. To address this issue, we introduce an iterative training approach in which the generated images are curated by humans and the most pleasing ones are fed back into the network to retrain. Additionally, w...
In recent years, Generative Adversarial Network (GAN) and its variants have gained great popularity ...
Expressing ideas in our minds which are inevitably visual into words had been a necessity. Lack of t...
Deep learning artificial neural networks are implemented in machines at an increasing rate in order ...
We live in a world made up of different objects, people, and environments interacting with each othe...
We live in a world made up of different objects, people, and environments interacting with each othe...
Generative adversarial networks (GANs) are a class of generative models, for which the goal is to le...
Since their introduction in 2014, Generative Adversarial Networks (GAN), have been a hot topic in th...
The object of research is image generation algorithms based on GAN. The article reviews the main use...
GANs (generative opposing networks) are a technique for learning deep representations in the absence...
GANs (generative opposing networks) are a technique for learning deep representations in the absence...
© 2019 Sukarna BaruaGenerative Adversarial Networks (GANs) are a powerful class of generative models...
Background: Generative Adversarial Networks (Goodfellow et al., 2014) (GANs)are the current state of...
Background: Generative Adversarial Networks (Goodfellow et al., 2014) (GANs)are the current state of...
Image synthesis is an important problem in computer vision and has many applications, such as comput...
Deep learning artificial neural networks are implemented in machines at an increasing rate in order ...
In recent years, Generative Adversarial Network (GAN) and its variants have gained great popularity ...
Expressing ideas in our minds which are inevitably visual into words had been a necessity. Lack of t...
Deep learning artificial neural networks are implemented in machines at an increasing rate in order ...
We live in a world made up of different objects, people, and environments interacting with each othe...
We live in a world made up of different objects, people, and environments interacting with each othe...
Generative adversarial networks (GANs) are a class of generative models, for which the goal is to le...
Since their introduction in 2014, Generative Adversarial Networks (GAN), have been a hot topic in th...
The object of research is image generation algorithms based on GAN. The article reviews the main use...
GANs (generative opposing networks) are a technique for learning deep representations in the absence...
GANs (generative opposing networks) are a technique for learning deep representations in the absence...
© 2019 Sukarna BaruaGenerative Adversarial Networks (GANs) are a powerful class of generative models...
Background: Generative Adversarial Networks (Goodfellow et al., 2014) (GANs)are the current state of...
Background: Generative Adversarial Networks (Goodfellow et al., 2014) (GANs)are the current state of...
Image synthesis is an important problem in computer vision and has many applications, such as comput...
Deep learning artificial neural networks are implemented in machines at an increasing rate in order ...
In recent years, Generative Adversarial Network (GAN) and its variants have gained great popularity ...
Expressing ideas in our minds which are inevitably visual into words had been a necessity. Lack of t...
Deep learning artificial neural networks are implemented in machines at an increasing rate in order ...