Add to ORKGGenerative Adversarial Networks (GANs) have achieved remarkable achievements in image synthesis. These successes of GANs rely on large scale datasets, requiring too much cost. With limited training data, how to stable the training process of GANs and generate realistic images have attracted more attention. The challenges of Data-Efficient GANs (DE-GANs) mainly arise from three aspects: (i) Mismatch Between Training and Target Distributions, (ii) Overfitting of the Discriminator, and (iii) Imbalance Between Latent and Data Spaces. Although many augmentation and pre-training strategies have been proposed to alleviate these issues, there lacks a systematic survey to summarize the properties, challenges, and solutions of DE-GANs. In this paper,...
Image generation has been heavily investigated in computer vision, where one core research challenge...
Image synthesis is an important problem in computer vision and has many applications, such as comput...
Generative adversarial networks (GANs) have demonstrated to be successful at generating realistic re...
Generative Adversarial Networks (GANs) are recently invented generative models which can produce hig...
Generative Adversarial Networks (GANs) have been workhorse generative models for last many years, es...
Generative Adversarial Networks (GANs) have recently introduced effective methods of performing Imag...
Data-Efficient GANs (DE-GANs), which aim to learn generative models with a limited amount of trainin...
The object of research is image generation algorithms based on GAN. The article reviews the main use...
© 2019 Sukarna BaruaGenerative Adversarial Networks (GANs) are a powerful class of generative models...
This paper focuses on one of the most fascinating and successful, but challenging generative models ...
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...
This paper investigates data synthesis with a Generative Adversarial Network (GAN) for augmenting th...
Generative Adversarial Networks (GANs) have proven to be efficient systems for data generation and o...
Generating high-quality and various image samples is a significant research goal in computer vision ...
Image generation has been heavily investigated in computer vision, where one core research challenge...
Image synthesis is an important problem in computer vision and has many applications, such as comput...
Generative adversarial networks (GANs) have demonstrated to be successful at generating realistic re...
Generative Adversarial Networks (GANs) are recently invented generative models which can produce hig...
Generative Adversarial Networks (GANs) have been workhorse generative models for last many years, es...
Generative Adversarial Networks (GANs) have recently introduced effective methods of performing Imag...
Data-Efficient GANs (DE-GANs), which aim to learn generative models with a limited amount of trainin...
The object of research is image generation algorithms based on GAN. The article reviews the main use...
© 2019 Sukarna BaruaGenerative Adversarial Networks (GANs) are a powerful class of generative models...
This paper focuses on one of the most fascinating and successful, but challenging generative models ...
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...
This paper investigates data synthesis with a Generative Adversarial Network (GAN) for augmenting th...
Generative Adversarial Networks (GANs) have proven to be efficient systems for data generation and o...
Generating high-quality and various image samples is a significant research goal in computer vision ...
Image generation has been heavily investigated in computer vision, where one core research challenge...
Image synthesis is an important problem in computer vision and has many applications, such as comput...
Generative adversarial networks (GANs) have demonstrated to be successful at generating realistic re...