13 citations · 26 across the 11 of their papers we have counts for
11 papers
Frugal Machine Learning for Energy-efficient, and Resource-aware Artificial Intelligence
John Violos, Konstantina-Christina Diamanti, Ioannis Kompatsiaris +1
Frugal Machine Learning (FML) refers to the practice of designing Machine Learning (ML) models that are efficient, cost-effective, and mindful of resource constraints. This field a…
Few-Shot Class-Incremental Learning For Efficient SAR Automatic Target Recognition
George Karantaidis, Athanasios Pantsios, Ioannis Kompatsiaris +1
Synthetic aperture radar automatic target recognition (SAR-ATR) systems have rapidly evolved to tackle incremental recognition challenges in operational settings. Data scarcity rem…
A Brief Review for Compression and Transfer Learning Techniques in DeepFake Detection
Andreas Karathanasis, John Violos, Ioannis Kompatsiaris +1
Training and deploying deepfake detection models on edge devices offers the advantage of maintaining data privacy and confidentiality by processing it close to its source. However,…
Reducing Inference Energy Consumption Using Dual Complementary CNNs
Michail Kinnas, John Violos, Ioannis Kompatsiaris +1
Energy efficiency of Convolutional Neural Networks (CNNs) has become an important area of research, with various strategies being developed to minimize the power consumption of the…
FaceX: Understanding Face Attribute Classifiers through Summary Model Explanations
Ioannis Sarridis, Christos Koutlis, Symeon Papadopoulos +1
EXplainable Artificial Intelligence (XAI) approaches are widely applied for identifying fairness issues in Artificial Intelligence (AI) systems. However, in the context of facial a…
Evaluating AI Group Fairness: a Fuzzy Logic Perspective
Emmanouil Krasanakis, Symeon Papadopoulos
Artificial intelligence systems often address fairness concerns by evaluating and mitigating measures of group discrimination, for example that indicate biases against certain gend…
Towards Optimal Trade-offs in Knowledge Distillation for CNNs and Vision Transformers at the Edge
John Violos, Symeon Papadopoulos, Ioannis Kompatsiaris
This paper discusses four facets of the Knowledge Distillation (KD) process for Convolutional Neural Networks (CNNs) and Vision Transformer (ViT) architectures, particularly when e…
Towards Standardizing AI Bias Exploration
Emmanouil Krasanakis, Symeon Papadopoulos
Creating fair AI systems is a complex problem that involves the assessment of context-dependent bias concerns. Existing research and programming libraries express specific concerns…
Credible, Unreliable or Leaked?: Evidence Verification for Enhanced Automated Fact-checking
Zacharias Chrysidis, Stefanos-Iordanis Papadopoulos, Symeon Papadopoulos +1
Automated fact-checking (AFC) is garnering increasing attention by researchers aiming to help fact-checkers combat the increasing spread of misinformation online. While many existi…
Towards Quantitative Evaluation of Explainable AI Methods for Deepfake Detection
Konstantinos Tsigos, Evlampios Apostolidis, Spyridon Baxevanakis +2
In this paper we propose a new framework for evaluating the performance of explanation methods on the decisions of a deepfake detector. This framework assesses the ability of an ex…
Synthetic Misinformers: Generating and Combating Multimodal Misinformation
Stefanos-Iordanis Papadopoulos, Christos Koutlis, Symeon Papadopoulos +1
With the expansion of social media and the increasing dissemination of multimedia content, the spread of misinformation has become a major concern. This necessitates effective stra…