22 citations · 96 across the 18 of their papers we have counts for
18 papers
FastCaps: A Design Methodology for Accelerating Capsule Network on Field Programmable Gate Arrays
Abdul Rahoof, Vivek Chaturvedi, Muhammad Shafique
Capsule Network (CapsNet) has shown significant improvement in understanding the variation in images along with better generalization ability compared to traditional Convolutional…
Scaling Model Checking for DNN Analysis via State-Space Reduction and Input Segmentation (Extended Version)
Mahum Naseer, Osman Hasan, Muhammad Shafique
Owing to their remarkable learning capabilities and performance in real-world applications, the use of machine learning systems based on Neural Networks (NNs) has been continuously…
Poster: Link between Bias, Node Sensitivity and Long-Tail Distribution in trained DNNs
Mahum Naseer, Muhammad Shafique
Owing to their remarkable learning (and relearning) capabilities, deep neural networks (DNNs) find use in numerous real-world applications. However, the learning of these data-driv…
UnbiasedNets: A Dataset Diversification Framework for Robustness Bias Alleviation in Neural Networks
Mahum Naseer, Bharath Srinivas Prabakaran, Osman Hasan +1
Performance of trained neural network (NN) models, in terms of testing accuracy, has improved remarkably over the past several years, especially with the advent of deep learning. H…
ISLE: A Framework for Image Level Semantic Segmentation Ensemble
Erik Ostrowski, Muhammad Shafique
One key bottleneck of employing state-of-the-art semantic segmentation networks in the real world is the availability of training labels. Conventional semantic segmentation network…
ReFit: A Framework for Refinement of Weakly Supervised Semantic Segmentation using Object Border Fitting for Medical Images
Bharath Srinivas Prabakaran, Erik Ostrowski, Muhammad Shafique
Weakly Supervised Semantic Segmentation (WSSS) relying only on image-level supervision is a promising approach to deal with the need for Segmentation networks, especially for gener…
TopSpark: A Timestep Optimization Methodology for Energy-Efficient Spiking Neural Networks on Autonomous Mobile Agents
Rachmad Vidya Wicaksana Putra, Muhammad Shafique
Autonomous mobile agents require low-power/energy-efficient machine learning (ML) algorithms to complete their ML-based tasks while adapting to diverse environments, as mobile agen…
AdvART: Adversarial Art for Camouflaged Object Detection Attacks
Amira Guesmi, Ioan Marius Bilasco, Muhammad Shafique +1
Physical adversarial attacks pose a significant practical threat as it deceives deep learning systems operating in the real world by producing prominent and maliciously designed ph…
Mantis: Enabling Energy-Efficient Autonomous Mobile Agents with Spiking Neural Networks
Rachmad Vidya Wicaksana Putra, Muhammad Shafique
Autonomous mobile agents such as unmanned aerial vehicles (UAVs) and mobile robots have shown huge potential for improving human productivity. These mobile agents require low power…
Building Resilience to Out-of-Distribution Visual Data via Input Optimization and Model Finetuning
Christopher J. Holder, Majid Khonji, Jorge Dias +1
A major challenge in machine learning is resilience to out-of-distribution data, that is data that exists outside of the distribution of a model's training data. Training is often…
On the Application of Efficient Neural Mapping to Real-Time Indoor Localisation for Unmanned Ground Vehicles
Christopher J. Holder, Muhammad Shafique
Global localisation from visual data is a challenging problem applicable to many robotics domains. Prior works have shown that neural networks can be trained to map images of an en…
AccelAT: A Framework for Accelerating the Adversarial Training of Deep Neural Networks through Accuracy Gradient
Farzad Nikfam, Alberto Marchisio, Maurizio Martina +1
Adversarial training is exploited to develop a robust Deep Neural Network (DNN) model against the malicious altered data. These attacks may have catastrophic effects on DNN models…
LaneSNNs: Spiking Neural Networks for Lane Detection on the Loihi Neuromorphic Processor
Alberto Viale, Alberto Marchisio, Maurizio Martina +2
Autonomous Driving (AD) related features represent important elements for the next generation of mobile robots and autonomous vehicles focused on increasingly intelligent, autonomo…
enpheeph: A Fault Injection Framework for Spiking and Compressed Deep Neural Networks
Alessio Colucci, Andreas Steininger, Muhammad Shafique
Research on Deep Neural Networks (DNNs) has focused on improving performance and accuracy for real-world deployments, leading to new models, such as Spiking Neural Networks (SNNs),…
tinySNN: Towards Memory- and Energy-Efficient Spiking Neural Networks
Rachmad Vidya Wicaksana Putra, Muhammad Shafique
Larger Spiking Neural Network (SNN) models are typically favorable as they can offer higher accuracy. However, employing such models on the resource- and energy-constrained embedde…
On Efficient Real-Time Semantic Segmentation: A Survey
Christopher J. Holder, Muhammad Shafique
Semantic segmentation is the problem of assigning a class label to every pixel in an image, and is an important component of an autonomous vehicle vision stack for facilitating sce…
fakeWeather: Adversarial Attacks for Deep Neural Networks Emulating Weather Conditions on the Camera Lens of Autonomous Systems
Alberto Marchisio, Giovanni Caramia, Maurizio Martina +1
Recently, Deep Neural Networks (DNNs) have achieved remarkable performances in many applications, while several studies have enhanced their vulnerabilities to malicious attacks. In…
lpSpikeCon: Enabling Low-Precision Spiking Neural Network Processing for Efficient Unsupervised Continual Learning on Autonomous Agents
Rachmad Vidya Wicaksana Putra, Muhammad Shafique
Recent advances have shown that SNN-based systems can efficiently perform unsupervised continual learning due to their bio-plausible learning rule, e.g., Spike-Timing-Dependent Pla…