Search Results for author:
Found 16 papers, 3 papers with code
SegLoc: Visual Self-supervised Learning Scheme for Dense Prediction Tasks of Security Inspection X-ray Images
Thus, here, we have considered dense prediction tasks on security inspection x-ray images to evaluate our proposed model Segmentation Localization (SegLoc).
On Continuity of Robust and Accurate Classifiers
To this end, we will introduce a framework for a rigorous study of harmonic and holomorphic hypothesis in learning theory terms and provide empirical evidence that continuous hypotheses does not perform as well as discontinuous hypotheses in some common machine learning tasks.
Spot The Odd One Out: Regularized Complete Cycle Consistent Anomaly Detector GAN
This study presents an adversarial method for anomaly detection in real-world applications, leveraging the power of generative adversarial neural networks (GANs) through cycle consistency in reconstruction error.
Ranked #1 on
Anomaly Detection
on SVHN
An Analytic Framework for Robust Training of Artificial Neural Networks
The reliability of a learning model is key to the successful deployment of machine learning in various industries.
Triple Motion Estimation and Frame Interpolation based on Adaptive Threshold for Frame Rate Up-Conversion
The proposed algorithm creates interpolated frames by first estimating motion vectors using unilateral (jointing forward and backward) and bilateral motion estimation.
Identity-Preserving Pose-Robust Face Hallucination Through Face Subspace Prior
Over the past few decades, numerous attempts have been made to address the problem of recovering a high-resolution (HR) facial image from its corresponding low-resolution (LR) counterpart, a task commonly referred to as face hallucination.
Xp-GAN: Unsupervised Multi-object Controllable Video Generation
Video Generation is a relatively new and yet popular subject in machine learning due to its vast variety of potential applications and its numerous challenges.
Modeling and Eliminating Adversarial Examples using Function Theory of Several Complex Variables
This paper presents a model and a solution for the existence and transfer of adversarial examples in analytic hypotheses.
Towards Explaining Adversarial Examples Phenomenon in Artificial Neural Networks
Also, we extend and unify some of the other proposals in the literature and provide alternative explanations on the observations made in those proposals.
Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks
We evaluate our method, referred to as Maximum Entropy Weighted Independent Set Pooling (MEWISPool), on graph classification tasks and the combinatorial optimization problem of the maximum independent set.
Ranked #2 on
Graph Classification
on FRANKENSTEIN
Abnormal Event Detection in Urban Surveillance Videos Using GAN and Transfer Learning
This paper is based on deep learning methods and provides an effective way to detect and locate abnormal events in videos by handling spatio temporal data.
Cross-domain recommender system using Generalized Canonical Correlation Analysis
In this representation we proposed an iterative method which applied MAX-VAR generalized canonical correlation analysis (GCCA) on users latent factors learned from matrix factorization on each domain.
Neither Global Nor Local: A Hierarchical Robust Subspace Clustering For Image Data
In this paper, we consider the problem of subspace clustering in presence of contiguous noise, occlusion and disguise.
Scalable and Robust Sparse Subspace Clustering Using Randomized Clustering and Multilayer Graphs
To improve the scalability of SSC, we propose to select a few sets of anchor points using a randomized hierarchical clustering method, and, for each set of anchor points, solve the LASSO problems for each data point allowing only anchor points to have a non-zero weight (this reduces drastically the number of variables).
An Efficient Evolutionary Based Method For Image Segmentation
In the final layer, an evolutionary algorithm is used to combine the resulted similar and neighbor regions.
Low Resolution Face Recognition Using a Two-Branch Deep Convolutional Neural Network Architecture
We propose a novel couple mappings method for low resolution face recognition using deep convolutional neural networks (DCNNs).
