most citedA Complete Survey on Contemporary Methods, Emerging Paradigms and Hybrid Approaches for Few-Shot Learning

3 citations · 4 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CR2025

Malware Detection in Docker Containers: An Image is Worth a Thousand Logs

Akis Nousias, Efklidis Katsaros, Evangelos Syrmos +7

Malware detection is increasingly challenged by evolving techniques like obfuscation and polymorphism, limiting the effectiveness of traditional methods. Meanwhile, the widespread…

cs.CV2024

Enhancing 3D Object Detection in Autonomous Vehicles Based on Synthetic Virtual Environment Analysis

Vladislav Li, Ilias Siniosoglou, Thomai Karamitsou +6

Autonomous Vehicles (AVs) use natural images and videos as input to understand the real world by overlaying and inferring digital elements, facilitating proactive detection in an e…

cs.LG2024

Enhancing Performance for Highly Imbalanced Medical Data via Data Regularization in a Federated Learning Setting

Georgios Tsoumplekas, Ilias Siniosoglou, Vasileios Argyriou +2

The increased availability of medical data has significantly impacted healthcare by enabling the application of machine / deep learning approaches in various instances. However, me…

cs.CL2024★ 1 cited

Benchmarking Advanced Text Anonymisation Methods: A Comparative Study on Novel and Traditional Approaches

Dimitris Asimopoulos, Ilias Siniosoglou, Vasileios Argyriou +6

In the realm of data privacy, the ability to effectively anonymise text is paramount. With the proliferation of deep learning and, in particular, transformer architectures, there i…

cs.LG2024

Evaluating the Energy Efficiency of Few-Shot Learning for Object Detection in Industrial Settings

Georgios Tsoumplekas, Vladislav Li, Ilias Siniosoglou +5

In the ever-evolving era of Artificial Intelligence (AI), model performance has constituted a key metric driving innovation, leading to an exponential growth in model size and comp…

cs.LG2024★ 3 cited

A Complete Survey on Contemporary Methods, Emerging Paradigms and Hybrid Approaches for Few-Shot Learning

Georgios Tsoumplekas, Vladislav Li, Panagiotis Sarigiannidis +1

Despite the widespread success of deep learning, its intense requirements for vast amounts of data and extensive training make it impractical for various real-world applications wh…