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Found 14 papers, 4 papers with code
Generative VS non-Generative Models in Engineering Shape Optimization
In this work, we perform a systematic comparison of the effectiveness and efficiency of generative and non-generative models in constructing design spaces for novel and efficient design exploration and shape optimization.
Action Segmentation Using 2D Skeleton Heatmaps
This paper presents a 2D skeleton-based action segmentation method with applications in fine-grained human activity recognition.
Privacy Enhancement for Cloud-Based Few-Shot Learning
Requiring less data for accurate models, few-shot learning has shown robustness and generality in many application domains.
Vehicle and License Plate Recognition with Novel Dataset for Toll Collection
The best Mean Average Precision (mAP@0. 5) of 98. 8% for vehicle type recognition, 98. 5% for license plate detection, and 98. 3% for license plate reading is achieved by YOLOv4, while its lighter version, i. e., Tiny YOLOv4 obtained a mAP of 97. 1%, 97. 4%, and 93. 7% on vehicle type recognition, license plate detection, and license plate reading, respectively.
Fake Visual Content Detection Using Two-Stream Convolutional Neural Networks
The proposed detector has demonstrated significant performance improvement compared to the current state-of-the-art fake content detectors and fusing the frequency and spatial domain streams has also improved generalization of the detector.
Intelligent Resource Allocation in Dense LoRa Networks using Deep Reinforcement Learning
The anticipated increase in the count of IoT devices in the coming years motivates the development of efficient algorithms that can help in their effective management while keeping the power consumption low.
Examining Machine Learning for 5G and Beyond through an Adversarial Lens
Spurred by the recent advances in deep learning to harness rich information hidden in large volumes of data and to tackle problems that are hard to model/solve (e. g., resource allocation problems), there is currently tremendous excitement in the mobile networks domain around the transformative potential of data-driven AI/ML based network automation, control and analytics for 5G and beyond.
Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning
Deep Reinforcement Learning (DRL) has numerous applications in the real world thanks to its outstanding ability in quickly adapting to the surrounding environments.
Black-box Adversarial ML Attack on Modulation Classification
We have evaluated the robustness of two famous such modulation classifiers (based on the techniques of convolutional neural networks and long short term memory) against adversarial machine learning attacks in black-box settings.
Learning-Driven Exploration for Reinforcement Learning
We introduce entropy-based exploration (EBE) that enables an agent to explore efficiently the unexplored regions of state space.
The Adversarial Machine Learning Conundrum: Can The Insecurity of ML Become The Achilles' Heel of Cognitive Networks?
We also provide some guidelines to design secure ML models for cognitive networks that are robust to adversarial attacks on the ML pipeline of cognitive networks.
Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward
Connected and autonomous vehicles (CAVs) will form the backbone of future next-generation intelligent transportation systems (ITS) providing travel comfort, road safety, along with a number of value-added services.
Towards Robust Neural Networks with Lipschitz Continuity
Deep neural networks have shown remarkable performance across a wide range of vision-based tasks, particularly due to the availability of large-scale datasets for training and better architectures.
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges
We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking.
