Affiliated Institutions
Related Publications
Deep Domain Confusion: Maximizing for Domain Invariance
Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias on a standard benchmark. Fine-tuning ...
A reliable multicast framework for light-weight sessions and application level framing
This paper describes SRM (Scalable Reliable Multicast), a reliable multicast framework for application level framing and light-weight sessions. The algorithms of this framework ...
RePaint: Inpainting using Denoising Diffusion Probabilistic Models
Free-form inpainting is the task of adding new content to an image in the regions specified by an arbitrary binary mask. Most existing approaches train for a certain distributio...
Quantized Convolutional Neural Networks for Mobile Devices
Recently, convolutional neural networks (CNN) have demonstrated impressive performance in various computer vision tasks. However, high performance hardware is typically indispen...
Semantics-Preserving Bag-of-Words Models and Applications
The Bag-of-Words (BoW) model is a promising image representation technique for image categorization and annotation tasks. One critical limitation of existing BoW models is that ...
Publication Info
- Year
- 2025
- Type
- article
- Citations
- 0
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
0
OpenAlex
Cite This
Amel Slim,
Mehdi Malah
(2025).
ACGAN: Adaptive Loss–Guided GAN–CNN Framework for Semantically Coherent Synthetic Insider Threat Data Generation.
Arabian Journal for Science and Engineering
.
https://doi.org/10.1007/s13369-025-10909-0
Identifiers
- DOI
- 10.1007/s13369-025-10909-0