A comprehensive survey of ai-generated content (aigc): A history of generative ai from gan to chatgpt
Recently, ChatGPT, along with DALL-E-2 and Codex, has been gaining significant attention
from society. As a result, many individuals have become interested in related resources and …
from society. As a result, many individuals have become interested in related resources and …
Machine learning methods for small data challenges in molecular science
B Dou, Z Zhu, E Merkurjev, L Ke, L Chen… - Chemical …, 2023 - ACS Publications
Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection
This study addresses the issue of fusing infrared and visible images that appear differently
for object detection. Aiming at generating an image of high visual quality, previous …
for object detection. Aiming at generating an image of high visual quality, previous …
A review on generative adversarial networks: Algorithms, theory, and applications
Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …
however, they have been studied since 2014, and a large number of algorithms have been …
Generative adversarial networks (GANs) challenges, solutions, and future directions
Generative Adversarial Networks (GANs) is a novel class of deep generative models that
has recently gained significant attention. GANs learn complex and high-dimensional …
has recently gained significant attention. GANs learn complex and high-dimensional …
Mutual information neural estimation
We argue that the estimation of mutual information between high dimensional continuous
random variables can be achieved by gradient descent over neural networks. We present a …
random variables can be achieved by gradient descent over neural networks. We present a …
A survey on generative adversarial networks for imbalance problems in computer vision tasks
Any computer vision application development starts off by acquiring images and data, then
preprocessing and pattern recognition steps to perform a task. When the acquired images …
preprocessing and pattern recognition steps to perform a task. When the acquired images …
Autogan: Neural architecture search for generative adversarial networks
Neural architecture search (NAS) has witnessed prevailing success in image classification
and (very recently) segmentation tasks. In this paper, we present the first preliminary study …
and (very recently) segmentation tasks. In this paper, we present the first preliminary study …
On data augmentation for GAN training
Recent successes in Generative Adversarial Networks (GAN) have affirmed the importance
of using more data in GAN training. Yet it is expensive to collect data in many domains such …
of using more data in GAN training. Yet it is expensive to collect data in many domains such …
Mine: mutual information neural estimation
We argue that the estimation of mutual information between high dimensional continuous
random variables can be achieved by gradient descent over neural networks. We present a …
random variables can be achieved by gradient descent over neural networks. We present a …