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Found 18 papers, 7 papers with code
Jedi: Entropy-based Localization and Removal of Adversarial Patches
Jedi tackles the patch localization problem from an information theory perspective; leverages two new ideas: (1) it improves the identification of potential patch regions using entropy analysis: we show that the entropy of adversarial patches is high, even in naturalistic patches; and (2) it improves the localization of adversarial patches, using an autoencoder that is able to complete patch regions from high entropy kernels.
Adversarial Attacks in a Multi-view Setting: An Empirical Study of the Adversarial Patches Inter-view Transferability
In this paper, we study the effect of view angle on the effectiveness of an adversarial patch.
A Few-shot Learning Graph Multi-Trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint
To the best of our knowledge, this is the first teacher-student architecture tailored for brain graph multi-trajectory growth prediction that is based on few-shot learning and generalized to graph neural networks (GNNs).
StairwayGraphNet for Inter- and Intra-modality Multi-resolution Brain Graph Alignment and Synthesis
Our SG-Net is grounded in three main contributions: (i) predicting a target graph from a source one based on a novel graph generative adversarial network in both inter (e. g., morphological-functional) and intra (e. g., functional-functional) domains, (ii) generating high-resolution brain graphs without resorting to the time consuming and expensive MRI processing steps, and (iii) enforcing the source distribution to match that of the ground truth graphs using an inter-modality aligner to relax the loss function to optimize.
Non-isomorphic Inter-modality Graph Alignment and Synthesis for Holistic Brain Mapping
Our three core contributions lie in (i) predicting a target graph (e. g., functional) from a source graph (e. g., morphological) based on a novel graph generative adversarial network (gGAN); (ii) using non-isomorphic graphs for both source and target domains with a different number of nodes, edges and structure; and (iii) enforcing the predicted target distribution to match that of the ground truth graphs using a graph autoencoder to relax the designed loss oprimization.
Graph Neural Networks in Network Neuroscience
Noninvasive medical neuroimaging has yielded many discoveries about the brain connectivity.
Brain Multigraph Prediction using Topology-Aware Adversarial Graph Neural Network
Brain graphs (i. e, connectomes) constructed from medical scans such as magnetic resonance imaging (MRI) have become increasingly important tools to characterize the abnormal changes in the human brain.
Towards New Multiwavelets: Associated Filters and Algorithms. Part I: Theoretical Framework and Investigation of Biomedical Signals, ECG and Coronavirus Cases
Biosignals are nowadays important subjects for scientific researches from both theory and applications especially with the appearance of new pandemics threatening humanity such as the new Coronavirus.
Topology-Aware Generative Adversarial Network for Joint Prediction of Multiple Brain Graphs from a Single Brain Graph
Several works based on Generative Adversarial Networks (GAN) have been recently proposed to predict a set of medical images from a single modality (e. g, FLAIR MRI from T1 MRI).
Supervised Multi-topology Network Cross-diffusion for Population-driven Brain Network Atlas Estimation
Estimating a representative and discriminative brain network atlas (BNA) is a nascent research field in mapping a population of brain networks in health and disease.
A Comparative Study of Filtering Approaches Applied to Color Archival Document Images
Current systems used by the Tunisian national archives for the automatic transcription of archival documents are hindered by many issues related to the performance of the optical character recognition (OCR) tools.
Adaptive strategy for superpixel-based region-growing image segmentation
From an initial contour-constrained over-segmentation of the input image, the image segmentation is achieved by iteratively merging similar superpixels into regions.
Dynamic Multiscale Tree Learning Using Ensemble Strong Classifiers for Multi-label Segmentation of Medical Images with Lesions
Unlike previous works that simply aggregate or cascade classifiers for addressing image segmentation and labeling tasks, we propose to embed strong classifiers into a tree structure that allows bi-directional flow of information between its classifier nodes to gradually improve their performances.
Comparative study and enhancement of Camera Tampering Detection algorithms
Recently the use of video surveillance systems is widely increasing.
Mouse Movement and Probabilistic Graphical Models Based E-Learning Activity Recognition Improvement Possibilistic Model
Probabilistic graphical models such as hidden Markov models and conditional random fields have been successfully used in order to identify a Web users activity.
A combined Approach Based on Fuzzy Classification and Contextual Region Growing to Image Segmentation
This allows the proposed approach to operate at high level instead of using low-level features and consequently to remedy to the problem of the semantic gap.
Unsupervised image segmentation by Global and local Criteria Optimization Based on Bayesian Networks
In the second phase, we model the superpixels by a Bayesian Network.
Dynamic Hierarchical Bayesian Network for Arabic Handwritten Word Recognition
This paper presents a new probabilistic graphical model used to model and recognize words representing the names of Tunisian cities.
