Author: Mahmoud Ghorbel

Mahmoud Ghorbel
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Mahmoud is a PhD researcher in machine learning. He also holds a bachelor's degree in physical science and a master's degree in telecommunications and networking systems. His current areas of research concern computer vision, stock market prediction and deep learning. He produced several scientific articles about person re- identification and the study of the robustness and stability of deep networks.

Meet ‘Chinese CLIP,’ An Implementation of CLIP Pretrained on Large-Scale Chinese Datasets with Contrastive Learning

AI Paper Summary
Contrastive Language-Image Pretraining, or CLIP, is a training method that differs from standard technics used by the vision community. A large dataset of over...

Latest Machine Learning Study Explains Background Bias in Deep Metric Learning DML by Conducting Multiple Experiments on Three Standard DML Datasets And Five Different...

AI Paper Summary
A computer system known as an image retrieval system is used to browse, search for, and retrieve images from a large database of digital...

Researchers At Fujitsu Use Deep Learning To Develop A Person Re-Identification (ReID) Method (ECANet) That Incorporates Obtained Pair Images Without The ID Labels Into...

AI Paper Summary
Over the last few years, video surveillance systems have experienced significant technological and economic expansion. In this context, the person re-identification (Re-ID) emerged. Re-ID...

Latest Artificial Intelligence (AI) Research Uses Synthetic Patient Data To Simulate A Machine Learning Enabled Learning Health System (LHS)

AI Paper Summary
In recent years, machine learning (ML) techniques in the medical field have grown significantly. Thanks to the high performance of deep learning models, machines...

Harvard Researchers Propose a Self-Supervised Deep Learning Algorithm for Fast and Scalable Search of Whole-Slide Images

AI Paper Summary
The necessity for accurate and economical gigapixel image analysis has risen as whole-slide imaging has become more widely used. Deep learning is at the...

Researchers at USC Propose Self-Attentive Pooling Method for Efficient Deep Learning

AI Paper Summary
In recent years, CNN architectures have made remarkable progress in various challenging vision tasks, including semantic segmentation and object classification. Large activation maps in...

Latest Machine Learning Research Proposes FP8 Binary Interchange Format: A Natural Progression For Accelerating Deep Learning Training Inference

AI Paper Summary
To meet the increasing computing needs of neural networks, AI processing requires full-stack innovation across hardware and software platforms. Using lower precision number formats...

Researchers Developed SmoothNets For Optimizing Convolutional Neural Network (CNN) Architecture Design For Differentially Private Deep Learning

AI Paper Summary
Differential privacy (DP) is used in machine learning to preserve the confidentiality of the information that forms the dataset. The most used algorithm to...

Researchers From China Construct A New GAN Adaption Framework For The Generalized One-Shot GAN Adaption Task

AI Paper Summary
The Generative Adversarial Network (GAN) is a deep neural network architecture that may learn from training data and produce new data that shares the...

A Cost-Sensitive Adversarial Data Augmentation (CSADA) Framework To Make Over-Parameterized Deep Learning Models Cost-Sensitive

AI Paper Summary
Most machine learning methods assume that each misclassification mistake a model makes is of equal severity. This is frequently not the case for unbalanced...

Understanding The Impact of Scaled Image on Robustness of Deep Neural Networks by Introducing a Scaling-Distortion Dataset ImageNet-CS

AI Paper Summary
In recent years, deep neural networks (DNNs) have proven their efficiency for computer vision applications, such as image classification and segmentation. The use of...

Researchers Analyze the Current Findings on Confidential Computing-Assisted Machine Learning ML Security and Privacy Techniques Along with the Limitations in Existing Trusted Execution Environment...

AI Paper Summary
The evolution of machine learning (ML) offers broader possibilities of use. However, wide applications also increase the risks of large attack surface on ML's...
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