A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …
large amount of data to achieve exceptional performance. Unfortunately, many applications …
A comprehensive review of generative adversarial networks: Fundamentals, applications, and challenges
M Megahed, A Mohammed - Wiley Interdisciplinary Reviews …, 2024 - Wiley Online Library
In machine learning, a generative model is responsible for generating new samples of data
in terms of a probabilistic model. Generative adversarial network (GAN) has been widely …
in terms of a probabilistic model. Generative adversarial network (GAN) has been widely …
EOSA-GAN: Feature enriched latent space optimized adversarial networks for synthesization of histopathology images using Ebola optimization search algorithm
ON Oyelade, AE Ezugwu - Biomedical Signal Processing and Control, 2023 - Elsevier
Generative adversarial networks (GAN) represent two deep learning (DL) models positioned
in an adversarial manner to generate and evaluate images. This area of research promises …
in an adversarial manner to generate and evaluate images. This area of research promises …
MISL: Multi-grained image-text semantic learning for text-guided image inpainting
Text-guided image inpainting aims to generate corrupted image patches and obtain a
plausible image based on textual descriptions, considering the relationship between textual …
plausible image based on textual descriptions, considering the relationship between textual …
Coloring anime line art videos with transformation region enhancement network
Automatic colorization of anime line art videos aims to produce color frames given line art
frames and reference color images, which is challenging due to various motions and …
frames and reference color images, which is challenging due to various motions and …
Modeling global distribution for federated learning with label distribution skew
Federated learning achieves joint training of deep models by connecting decentralized
datasources, which can significantly mitigate the risk of privacy leakage. However, in a more …
datasources, which can significantly mitigate the risk of privacy leakage. However, in a more …
Dynamic adaptive generative adversarial networks with multi-view temporal factorizations for hybrid recovery of missing traffic data
Making reliable recovery of missing traffic data facilitates diverse applications of data-driven
intelligent transportation system. But faced with correlation and heterogeneity along spatial …
intelligent transportation system. But faced with correlation and heterogeneity along spatial …
Reparameterizing and dynamically quantizing image features for image generation
For autoregressive image generation, vector-quantized VAEs (VQ-VAEs) quantize image
features with discrete codebook entries and reconstruct images from quantized features …
features with discrete codebook entries and reconstruct images from quantized features …
Subgraph generation applied in GraphSAGE deal with imbalanced node classification
K Huang, C Chen - Soft Computing, 2024 - Springer
In graph neural network applications, GraphSAGE applies inductive learning and has been
widely applied in important research topics such as node classification. The subgraph of …
widely applied in important research topics such as node classification. The subgraph of …
[HTML][HTML] SaltGAN: A feature-infused and loss-controlled generative adversarial network with preserved checkpoints for evolving histopathology images
ON Oyelade, H Wang, SA Adewuyi - Biomedical Signal Processing and …, 2024 - Elsevier
The use of natural phenomena as inspiration to address real-life problems has become an
increasingly popular research approach. In the medical domain, generative adversarial …
increasingly popular research approach. In the medical domain, generative adversarial …