Advances, challenges and opportunities in creating data for trustworthy AI
As artificial intelligence (AI) transitions from research to deployment, creating the appropriate
datasets and data pipelines to develop and evaluate AI models is increasingly the biggest …
datasets and data pipelines to develop and evaluate AI models is increasingly the biggest …
[HTML][HTML] Machine learning and deep learning based predictive quality in manufacturing: a systematic review
With the ongoing digitization of the manufacturing industry and the ability to bring together
data from manufacturing processes and quality measurements, there is enormous potential …
data from manufacturing processes and quality measurements, there is enormous potential …
Fine-grained image analysis with deep learning: A survey
Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer
vision and pattern recognition, and underpins a diverse set of real-world applications. The …
vision and pattern recognition, and underpins a diverse set of real-world applications. The …
Ai-generated content (aigc): A survey
To address the challenges of digital intelligence in the digital economy, artificial intelligence-
generated content (AIGC) has emerged. AIGC uses artificial intelligence to assist or replace …
generated content (AIGC) has emerged. AIGC uses artificial intelligence to assist or replace …
[HTML][HTML] Survey on synthetic data generation, evaluation methods and GANs
A Figueira, B Vaz - Mathematics, 2022 - mdpi.com
Synthetic data consists of artificially generated data. When data are scarce, or of poor
quality, synthetic data can be used, for example, to improve the performance of machine …
quality, synthetic data can be used, for example, to improve the performance of machine …
Synthetic Data--what, why and how?
This explainer document aims to provide an overview of the current state of the rapidly
expanding work on synthetic data technologies, with a particular focus on privacy. The …
expanding work on synthetic data technologies, with a particular focus on privacy. The …
Machine learning for synthetic data generation: a review
Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …
data-related issues. These include data of poor quality, insufficient data points leading to …
Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding
For many fundamental scene understanding tasks, it is difficult or impossible to obtain per-
pixel ground truth labels from real images. We address this challenge by introducing …
pixel ground truth labels from real images. We address this challenge by introducing …
[HTML][HTML] 6G enabled smart infrastructure for sustainable society: Opportunities, challenges, and research roadmap
The 5G wireless communication network is currently faced with the challenge of limited data
speed exacerbated by the proliferation of billions of data-intensive applications. To address …
speed exacerbated by the proliferation of billions of data-intensive applications. To address …
[HTML][HTML] Review of artificial intelligence and machine learning technologies: classification, restrictions, opportunities and challenges
RI Mukhamediev, Y Popova, Y Kuchin, E Zaitseva… - Mathematics, 2022 - mdpi.com
Artificial intelligence (AI) is an evolving set of technologies used for solving a wide range of
applied issues. The core of AI is machine learning (ML)—a complex of algorithms and …
applied issues. The core of AI is machine learning (ML)—a complex of algorithms and …