Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Machine learning methods for small data challenges in molecular science
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 …
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 …
Imperfect ImaGANation: Implications of GANs exacerbating biases on facial data augmentation and snapchat face lenses
In this paper, we show that popular Generative Adversarial Network (GAN) variants
exacerbate biases along the axes of gender and skin tone in the generated data. The use of …
exacerbate biases along the axes of gender and skin tone in the generated data. The use of …
MASCDB, a database of images, descriptors and microphysical properties of individual snowflakes in free fall
Snowfall information at the scale of individual particles is rare, difficult to gather, but
fundamental for a better understanding of solid precipitation microphysics. In this article we …
fundamental for a better understanding of solid precipitation microphysics. In this article we …
Improving generative adversarial networks via adversarial learning in latent space
Abstract For Generative Adversarial Networks which map a latent distribution to the target
distribution, in this paper, we study how the sampling in latent space can affect the …
distribution, in this paper, we study how the sampling in latent space can affect the …
Improving clustergan using self-augmented information maximization of disentangling latent spaces
Since their introduction in the last few years, conditional generative models have seen
remarkable achievements. However, they often need the use of large amounts of labelled …
remarkable achievements. However, they often need the use of large amounts of labelled …
Searching towards class-aware generators for conditional generative adversarial networks
Conditional generative adversarial networks (cGANs) are designed to generate images
based on the provided conditions, eg., class-level distributions, semantic label maps, etc …
based on the provided conditions, eg., class-level distributions, semantic label maps, etc …
Self-supervised augmentation of quality data based on classification-reinforced GAN
SH Kim, S Lee - 2023 17th International Conference on …, 2023 - ieeexplore.ieee.org
In deep learning, the quality of ground truth training data is crucial for the resulting
performance. However, depending on applications, collecting a sufficient amount of quality …
performance. However, depending on applications, collecting a sufficient amount of quality …
Survey of generative adversarial network.
W Zhenglong, Z BaoWen - Chinese Journal of Network & …, 2021 - search.ebscohost.com
Firstly, the basic theory, application scenarios and current state of research of GAN
(generative adversarial network) were introduced, and the problems need to be improved …
(generative adversarial network) were introduced, and the problems need to be improved …
Sequence Modeling Based Data Augmentation for Micro-expression Recognition
X Lin, S Ai, J Gao, J He, L Yan, J Zhang… - International Forum on …, 2023 - Springer
Micro-expressions (MEs) can reveal people's true emotions and expose deceitful behaviors.
With the introduction of deep learning, the accuracy of micro-expression recognition (MER) …
With the introduction of deep learning, the accuracy of micro-expression recognition (MER) …