Search Results for author:
Found 140 papers, 45 papers with code
Deep Learning for Camera Calibration and Beyond: A Survey
In this paper, we provide a comprehensive survey of learning-based camera calibration techniques, by analyzing their strengths and limitations.
Deep Adaptive Input Normalization for Time Series Forecasting
Deep Learning (DL) models can be used to tackle time series analysis tasks with great success.
Self-attention fusion for audiovisual emotion recognition with incomplete data
In this paper, we consider the problem of multimodal data analysis with a use case of audiovisual emotion recognition.
Ranked #1 on
Facial Emotion Recognition
on RAVDESS
Blind ECG Restoration by Operational Cycle-GANs
Usually, a set of such artifacts occur on the same ECG signal with varying severity and duration, and this makes an accurate diagnosis by machines or medical doctors extremely difficult.
Zero-Shot Motor Health Monitoring by Blind Domain Transition
To address this need, in this pilot study, we propose a zero-shot bearing fault detection method that can detect any fault on a new (target) machine regardless of the working conditions, sensor parameters, or fault characteristics.
Self-Organized Operational Neural Networks with Generative Neurons
However, Greedy Iterative Search (GIS) method, which is the search method used to find optimal operators in ONNs takes many training sessions to find a single operator set per layer.
FastONN -- Python based open-source GPU implementation for Operational Neural Networks
Operational Neural Networks (ONNs) have recently been proposed as a special class of artificial neural networks for grid structured data.
Heterogeneous Multilayer Generalized Operational Perceptron
Previously, Generalized Operational Perceptron (GOP) was proposed to extend conventional perceptron model by defining a diverse set of neuronal activities to imitate a generalized model of biological neurons.
Progressive Operational Perceptron with Memory
Generalized Operational Perceptron (GOP) was proposed to generalize the linear neuron model in the traditional Multilayer Perceptron (MLP) and this model can mimic the synaptic connections of the biological neurons that have nonlinear neurochemical behaviours.
A Personalized Zero-Shot ECG Arrhythmia Monitoring System: From Sparse Representation Based Domain Adaption to Energy Efficient Abnormal Beat Detection for Practical ECG Surveillance
An extensive set of experiments performed on the benchmark MIT-BIH ECG dataset shows that when this domain adaptation-based training data generator is used with a simple 1-D CNN classifier, the method outperforms the prior work by a significant margin.
Ranked #2 on
Arrhythmia Detection
on MIT-BIH Arrhythmia Database
Complexity Analysis Of Next-Generation VVC Encoding and Decoding
While the next generation video compression standard, Versatile Video Coding (VVC), provides a superior compression efficiency, its computational complexity dramatically increases.
Multimedia Computational Complexity Image and Video Processing
Early Myocardial Infarction Detection over Multi-view Echocardiography
Following the blockage of a coronary artery, the regional wall motion abnormality (RWMA) of the ischemic myocardial segments is the earliest change to set in.
Representation Based Regression for Object Distance Estimation
To the best of our knowledge, this is the first representation-based method proposed for performing a regression task by utilizing the modified CSENs; and hence, we name this novel approach as Representation-based Regression (RbR).
Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks
Although numerous R-peak detectors have been proposed in the literature, their robustness and performance levels may significantly deteriorate in low-quality and noisy signals acquired from mobile electrocardiogram (ECG) sensors, such as Holter monitors.
SRL-SOA: Self-Representation Learning with Sparse 1D-Operational Autoencoder for Hyperspectral Image Band Selection
The band selection in the hyperspectral image (HSI) data processing is an important task considering its effect on the computational complexity and accuracy.
Bag of Color Features For Color Constancy
To further improve the illumination estimation accuracy, we propose a novel attention mechanism for the BoCF model with two variants based on self-attention.
INTEL-TAU: A Color Constancy Dataset
In this paper, we describe a new large dataset for illumination estimation.
Few-Shot Camera-Adaptive Color Constancy
Image Declipping
+1
AnomalyHop: An SSL-based Image Anomaly Localization Method
An image anomaly localization method based on the successive subspace learning (SSL) framework, called AnomalyHop, is proposed in this work.
Multilinear Compressive Learning
Compressive Learning is an emerging topic that combines signal acquisition via compressive sensing and machine learning to perform inference tasks directly on a small number of measurements.
Robust channel-wise illumination estimation
We test this approach on the proposed method and show that it can indeed be used to avoid several extreme error cases and, thus, improves the practicality of the proposed technique.
Multimodal Subspace Support Vector Data Description
In this paper, we propose a novel method for projecting data from multiple modalities to a new subspace optimized for one-class classification.
Learning to ignore: rethinking attention in CNNs
Based on this idea, we propose to reformulate the attention mechanism in CNNs to learn to ignore instead of learning to attend.
Subspace Support Vector Data Description
The method iteratively optimizes the data mapping along with data description in order to define a compact class representation in a low-dimensional feature space.
Speed-up and multi-view extensions to Subclass Discriminant Analysis
We show that by exploiting the structure of the between-class Laplacian matrix, the eigendecomposition step can be substituted with a much faster process.
Knowledge Distillation By Sparse Representation Matching
Knowledge Distillation refers to a class of methods that transfers the knowledge from a teacher network to a student network.
Convolutional versus Self-Organized Operational Neural Networks for Real-World Blind Image Denoising
Real-world blind denoising poses a unique image restoration challenge due to the non-deterministic nature of the underlying noise distribution.
Blind Restoration of Real-World Audio by 1D Operational GANs
In this study, we propose a novel approach for blind restoration of real-world audio signals by Operational Generative Adversarial Networks (Op-GANs) with temporal and spectral objective metrics to enhance the quality of restored audio signal regardless of the type and severity of each artifact corrupting it.
MAMIQA: No-Reference Image Quality Assessment Based on Multiscale Attention Mechanism With Natural Scene Statistics
No-Reference Image Quality Assessment aims to evaluate the perceptual quality of an image, according to human perception.
Attention-based Neural Bag-of-Features Learning for Sequence Data
In this paper, we propose 2D-Attention (2DA), a generic attention formulation for sequence data, which acts as a complementary computation block that can detect and focus on relevant sources of information for the given learning objective.
Advance Warning Methodologies for COVID-19 using Chest X-Ray Images
The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage.
Bilinear Input Normalization for Neural Networks in Financial Forecasting
Data normalization is one of the most important preprocessing steps when building a machine learning model, especially when the model of interest is a deep neural network.
Operational Neural Networks for Parameter-Efficient Hyperspectral Single-Image Super-Resolution
This work focuses on extending the convolutional filters of a popular super-resolution model to more powerful operational filters to enhance the model performance on hyperspectral images.
Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis
Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature of the market.
Multilinear Compressive Learning with Prior Knowledge
Extensive experiments demonstrate that the proposed knowledge transfer method can effectively train MCL models to compressively sense and synthesize better features for the learning tasks with improved performances, especially when the complexity of the learning task increases.
Ellipsoidal Subspace Support Vector Data Description
In this paper, we propose a novel method for transforming data into a low-dimensional space optimized for one-class classification.
Graph-Embedded Subspace Support Vector Data Description
In this paper, we propose a novel subspace learning framework for one-class classification.
R2C-GAN: Restore-to-Classify GANs for Blind X-Ray Restoration and COVID-19 Classification
The proposed restoration approach achieves over 90% F1-Score which is significantly higher than the performance of any deep model.
Temporal Attention Augmented Bilinear Network for Financial Time Series Data Analysis
Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature of the market.
Dropout Concrete Autoencoder for Band Selection on HSI Scenes
Deep learning-based informative band selection methods on hyperspectral images (HSI) recently have gained intense attention to eliminate spectral correlation and redundancies.
Weighted Linear Discriminant Analysis based on Class Saliency Information
In this paper, we propose a new variant of Linear Discriminant Analysis to overcome underlying drawbacks of traditional LDA and other LDA variants targeting problems involving imbalanced classes.
Deep Multi-view Learning to Rank
We study the problem of learning to rank from multiple information sources.
Human experts vs. machines in taxa recognition
Contrary to previous findings in the literature, we find that for machines following a typical flat classification approach commonly used in machine learning performs better than forcing machines to adopt a hierarchical, local per parent node approach used by human taxonomic experts ($\overline{CE}=13. 8\%$).
Tensor Representation in High-Frequency Financial Data for Price Change Prediction
Nowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high frequency traders.
Multilinear Class-Specific Discriminant Analysis
There has been a great effort to transfer linear discriminant techniques that operate on vector data to high-order data, generally referred to as Multilinear Discriminant Analysis (MDA) techniques.
Improving Efficiency in Convolutional Neural Network with Multilinear Filters
The excellent performance of deep neural networks has enabled us to solve several automatization problems, opening an era of autonomous devices.
Neural Class-Specific Regression for face verification
Face verification is a problem approached in the literature mainly using nonlinear class-specific subspace learning techniques.
Generalized Multi-view Embedding for Visual Recognition and Cross-modal Retrieval
In this paper, the problem of multi-view embedding from different visual cues and modalities is considered.
INTEL-TUT Dataset for Camera Invariant Color Constancy Research
In this paper, we provide a novel dataset designed for camera invariant color constancy research.
Probabilistic Saliency Estimation
In this paper, we model the salient object detection problem under a probabilistic framework encoding the boundary connectivity saliency cue and smoothness constraints in an optimization problem.
Video Ladder Networks
Thanks to the recurrent connections, the decoder can exploit temporal summaries generated from all layers of the encoder.
On the Dynamics of a Recurrent Hopfield Network
In this research paper novel real/complex valued recurrent Hopfield Neural Network (RHNN) is proposed.
Finding Better Topologies for Deep Convolutional Neural Networks by Evolution
In this paper, we propose an evolutionary strategy to find better topologies for deep CNNs.
Machine Learning for Forecasting Mid Price Movement using Limit Order Book Data
Forecasting the movements of stock prices is one the most challenging problems in financial markets analysis.
Compressively Sensed Image Recognition
Recent literature works show that compressive image classification is possible in CS domain without reconstruction of the signal.
Using Deep Learning for price prediction by exploiting stationary limit order book features
The recent surge in Deep Learning (DL) research of the past decade has successfully provided solutions to many difficult problems.
Limited Random Walk Algorithm for Big Graph Data Clustering
Graph clustering is an important technique to understand the relationships between the vertices in a big graph.
Social and Information Networks Physics and Society
Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data
However, combining existing BoF formulations with deep feature extractors pose significant challenges: the distribution of the input features is not stationary, tuning the hyper-parameters of the model can be especially difficult and the normalizations involved in the BoF model can cause significant instabilities during the training process.
Operational Neural Networks
In order to address this drawback and also to accomplish a more generalized model over the convolutional neurons, this study proposes a novel network model, called Operational Neural Networks (ONNs), which can be heterogeneous and encapsulate neurons with any set of operators to boost diversity and to learn highly complex and multi-modal functions or spaces with minimal network complexity and training data.
1D Convolutional Neural Network Models for Sleep Arousal Detection
Sleep arousals transition the depth of sleep to a more superficial stage.
Data-driven Neural Architecture Learning For Financial Time-series Forecasting
Forecasting based on financial time-series is a challenging task since most real-world data exhibits nonstationary property and nonlinear dependencies.
Colorectal cancer diagnosis from histology images: A comparative study
Moreover, we conduct quantitative comparative evaluations among the traditional methods, transfer learning-based methods and the proposed adaptive approach for the particular task of cancer detection and identification from scarce and low-resolution histology images.
3D Quantum Cuts for Automatic Segmentation of Porous Media in Tomography Images
Binary segmentation of volumetric images of porous media is a crucial step towards gaining a deeper understanding of the factors governing biogeochemical processes at minute scales.
Feature Engineering for Mid-Price Prediction with Deep Learning
Mid-price movement prediction based on limit order book (LOB) data is a challenging task due to the complexity and dynamics of the LOB.
1D Convolutional Neural Networks and Applications: A Survey
During the last decade, Convolutional Neural Networks (CNNs) have become the de facto standard for various Computer Vision and Machine Learning operations.
Color Constancy Convolutional Autoencoder
In this paper, we study the importance of pre-training for the generalization capability in the color constancy problem.
Reversible Privacy Preservation using Multi-level Encryption and Compressive Sensing
Security monitoring via ubiquitous cameras and their more extended in intelligent buildings stand to gain from advances in signal processing and machine learning.
Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators
Stock price prediction is a challenging task, but machine learning methods have recently been used successfully for this purpose.
Neural Architecture Search by Estimation of Network Structure Distributions
Our matrix of probabilities is equivalent to the population of models, but allows for discovery of structural irregularities, while being simple to interpret and analyze.
Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network
Results: The proposed algorithm is validated on the 2018 PhysioNet challenge dataset.
Automatic image-based identification and biomass estimation of invertebrates
We use this database to test the classification accuracy i. e. how well the species identity of a specimen can be predicted from images taken by the machine.
Incremental Fast Subclass Discriminant Analysis
This paper proposes an incremental solution to Fast Subclass Discriminant Analysis (fastSDA).
Subset Sampling For Progressive Neural Network Learning
Progressive Neural Network Learning is a class of algorithms that incrementally construct the network's topology and optimize its parameters based on the training data.
Convolutional Sparse Support Estimator Network (CSEN) From energy efficient support estimation to learning-aided Compressive Sensing
Indeed, a vast majority of them use sparse signal recovery techniques to obtain support sets instead of directly mapping the non-zero locations from denser measurements (e. g., Compressively Sensed Measurements).
Not all domains are equally complex: Adaptive Multi-Domain Learning
The most common approach in multi-domain learning is to form a domain agnostic model, the parameters of which are shared among all domains, and learn a small number of extra domain-specific parameters for each individual new domain.
A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications
Monitoring structural damage is extremely important for sustaining and preserving the service life of civil structures.
Saliency-based Weighted Multi-label Linear Discriminant Analysis
The proposed method is based on a probabilistic model for defining the weights of individual samples in a weighted multi-label LDA approach.
Probabilistic Color Constancy
In this paper, we propose a novel unsupervised color constancy method, called Probabilistic Color Constancy (PCC).
Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images
Any technological tool that can be provided to healthcare practitioners to save time, effort, and possibly lives has crucial importance.
Monte Carlo Dropout Ensembles for Robust Illumination Estimation
Computational color constancy is a preprocessing step used in many camera systems.
Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection
It further enables medical experts to gain an enhanced visualization capability of echo images through color-coded segments along with their "maximum motion displacement" plots helping them to better assess wall motion and LV Ejection-Fraction (LVEF).
Collaborative Multi-Robot Systems for Search and Rescue: Coordination and Perception
Autonomous or teleoperated robots have been playing increasingly important roles in civil applications in recent years.
Self-Organized Operational Neural Networks for Severe Image Restoration Problems
Discriminative learning based on convolutional neural networks (CNNs) aims to perform image restoration by learning from training examples of noisy-clean image pairs.
Graph Embedding with Data Uncertainty
spectral-based subspace learning is a common data preprocessing step in many machine learning pipelines.
Operational vs Convolutional Neural Networks for Image Denoising
Convolutional Neural Networks (CNNs) have recently become a favored technique for image denoising due to its adaptive learning ability, especially with a deep configuration.
Exploiting Heterogeneity in Operational Neural Networks by Synaptic Plasticity
As a heterogenous network model, ONNs are based on a generalized neuron model that can encapsulate any set of non-linear operators to boost diversity and to learn highly complex and multi-modal functions or spaces with minimal network complexity and training data.
Performance Indicator in Multilinear Compressive Learning
Recently, the Multilinear Compressive Learning (MCL) framework was proposed to efficiently optimize the sensing and learning steps when working with multidimensional signals, i. e. tensors.
COVID-19 Infection Map Generation and Detection from Chest X-Ray Images
To accomplish this, we have compiled the largest dataset with 119, 316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human-machine approach.
ON NEURAL NETWORK GENERALIZATION VIA PROMOTING WITHIN-LAYER ACTIVATION DIVERSITY
During the last decade, neural networks have been intensively used to tackle various problems and they have often led to state-of-the-art results.
Early Detection of Myocardial Infarction in Low-Quality Echocardiography
Myocardial infarction (MI), or commonly known as heart attack, is a life-threatening health problem worldwide from which 32. 4 million people suffer each year.
Classification of Polarimetric SAR Images Using Compact Convolutional Neural Networks
In this work, to address the limitations of traditional ML and deep CNN based methods, a novel and systematic classification framework is proposed for the classification of PolSAR images, based on a compact and adaptive implementation of CNNs using a sliding-window classification approach.
Speech Command Recognition in Computationally Constrained Environments with a Quadratic Self-organized Operational Layer
Automatic classification of speech commands has revolutionized human computer interactions in robotic applications.
Robust R-Peak Detection in Low-Quality Holter ECGs using 1D Convolutional Neural Network
Results also demonstrate similar or better performance than most competing algorithms on MIT-DB with 99. 83% F1-score, 99. 85% recall, and 99. 82% precision.
Reliable COVID-19 Detection Using Chest X-ray Images
To address this need, in this study, we propose a reliable COVID-19 detection network: ReCovNet, which can discriminate COVID-19 pneumonia from 14 different thoracic diseases and healthy subjects.
Ensembling object detectors for image and video data analysis
In this paper, we propose a method for ensembling the outputs of multiple object detectors for improving detection performance and precision of bounding boxes on image data.
BM3D vs 2-Layer ONN
Despite their recent success on image denoising, the need for deep and complex architectures still hinders the practical usage of CNNs.
Learning distinct features helps, provably
We study the diversity of the features learned by a two-layer neural network trained with the least squares loss.
Generalized Tensor Summation Compressive Sensing Network (GTSNET): An Easy to Learn Compressive Sensing Operation
For a special case where the CS operation is set as a single tensor multiplication, the model is reduced to the learning-based separable CS; while a dense CS matrix can be approximated and learned as the summation of multiple tensors.
Remote Multilinear Compressive Learning with Adaptive Compression
By developing compressive sensing and learning models that can operate with an adaptive compression rate, we can maximize the informational content throughput of the whole application.
Super Neurons
In this article, we present superior (generative) neuron models (or super neurons in short) that allow random or learnable kernel shifts and thus can increase the receptive field size of each connection.
Real-Time Glaucoma Detection from Digital Fundus Images using Self-ONNs
Glaucoma leads to permanent vision disability by damaging the optical nerve that transmits visual images to the brain.
Early Bearing Fault Diagnosis of Rotating Machinery by 1D Self-Organized Operational Neural Networks
Preventive maintenance of modern electric rotating machinery (RM) is critical for ensuring reliable operation, preventing unpredicted breakdowns and avoiding costly repairs.
Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks
Despite the proliferation of numerous deep learning methods proposed for generic ECG classification and arrhythmia detection, compact systems with the real-time ability and high accuracy for classifying patient-specific ECG are still few.
Improving Neural Network Generalization via Promoting Within-Layer Diversity
Neural networks are composed of multiple layers arranged in a hierarchical structure jointly trained with a gradient-based optimization, where the errors are back-propagated from the last layer back to the first one.
Image denoising by Super Neurons: Why go deep?
As the integration of non-local information is known to benefit denoising, in this work we investigate the use of super neurons for both synthetic and real-world image denoising.
Self-Attention Neural Bag-of-Features
In this work, we propose several attention formulations for multivariate sequence data.
Reducing Redundancy in the Bottleneck Representation of the Autoencoders
We tested our approach across different tasks: dimensionality reduction using three different dataset, image compression using the MNIST dataset, and image denoising using fashion MNIST.
Non-Linear Spectral Dimensionality Reduction Under Uncertainty
In this paper, we consider the problem of non-linear dimensionality reduction under uncertainty, both from a theoretical and algorithmic perspectives.
OSegNet: Operational Segmentation Network for COVID-19 Detection using Chest X-ray Images
To address the data scarcity encountered in training and especially in evaluation, this study extends the largest COVID-19 CXR dataset: QaTa-COV19 with 121, 378 CXRs including 9258 COVID-19 samples with their corresponding ground-truth segmentation masks that are publicly shared with the research community.
Global ECG Classification by Self-Operational Neural Networks with Feature Injection
The classification layers can thus benefit from both temporal and learned features for the final arrhythmia classification.
Early Myocardial Infarction Detection with One-Class Classification over Multi-view Echocardiography
In this study, we propose a framework for early detection of MI over multi-view echocardiography that leverages one-class classification (OCC) techniques.
Efficient CNN with uncorrelated Bag of Features pooling
In this paper, we propose an approach that builds on top of BoF pooling to boost its efficiency by ensuring that the items of the learned dictionary are non-redundant.
End-to-end Transformer for Compressed Video Quality Enhancement
In this work, we propose a transformer-based compressed video quality enhancement (TVQE) method, consisting of Swin-AutoEncoder based Spatio-Temporal feature Fusion (SSTF) module and Channel-wise Attention based Quality Enhancement (CAQE) module.
WLD-Reg: A Data-dependent Within-layer Diversity Regularizer
At each optimization step, neurons at a given layer receive feedback from neurons belonging to higher layers of the hierarchy.
Real-Time Damage Detection in Fiber Lifting Ropes Using Convolutional Neural Networks
The health and safety hazards posed by worn crane lifting ropes mandate periodic inspection for damage.
Hyperspectral Image Analysis with Subspace Learning-based One-Class Classification
Considering the imbalanced labels of the LULC classification problem and rich spectral information (high number of dimensions), the proposed classification approach is well-suited for HSI data.
Improved Active Fire Detection using Operational U-Nets
As a consequence of global warming and climate change, the risk and extent of wildfires have been increasing in many areas worldwide.
Optimum Output Long Short-Term Memory Cell for High-Frequency Trading Forecasting
This high-paced stock price forecasting is usually based on vectors that need to be treated as sequential and time-independent signals due to the time irregularities that are inherent in high-frequency trading.
On Feature Diversity in Energy-based Models
Energy-based learning is a powerful learning paradigm that encapsulates various discriminative and generative approaches.
Operational Support Estimator Networks
In this work, we propose a novel approach called Operational Support Estimator Networks (OSENs) for the support estimation task.
Convolutional autoencoder-based multimodal one-class classification
We show that such regularizers improve performance.
One-Class Classification for Intrusion Detection on Vehicular Networks
Research has been done on the efficacy of these methods, most notably One-Class Support Vector Machine and Support Vector Data Description, but many new extensions of these works have been proposed and have yet to be tested for injection attacks in vehicular networks.
Newton Method-based Subspace Support Vector Data Description
The objective of S-SVDD is to map the original data to a subspace optimized for one-class classification, and the iterative optimization process of data mapping and description in S-SVDD relies on gradient descent.
Credit Card Fraud Detection with Subspace Learning-based One-Class Classification
The study highlights the potential of subspace learning-based OCC algorithms by investigating the limitations of current fraud detection strategies and the specific challenges of credit card fraud detection.
SAF-Net: Self-Attention Fusion Network for Myocardial Infarction Detection using Multi-View Echocardiography
Myocardial infarction (MI) is a severe case of coronary artery disease (CAD) and ultimately, its detection is substantial to prevent progressive damage to the myocardium.
Cryptocurrency Portfolio Optimization by Neural Networks
Many cryptocurrency brokers nowadays offer a variety of derivative assets that allow traders to perform hedging or speculation.
Improving Unimodal Inference with Multimodal Transformers
Interestingly, we also observe that optimization of the unimodal branches improves the multimodal branch, compared to a similar multimodal model trained from scratch.
Real-Time Vibration-Based Bearing Fault Diagnosis Under Time-Varying Speed Conditions
Detection of rolling-element bearing faults is crucial for implementing proactive maintenance strategies and for minimizing the economic and operational consequences of unexpected failures.
Exploring Sound vs Vibration for Robust Fault Detection on Rotating Machinery
One major reason is the lack of a benchmark dataset providing a large volume of both vibration and sound data over several working conditions for different machines and sensor locations.
Quadratic Time-Frequency Analysis of Vibration Signals for Diagnosing Bearing Faults
Diagnosis of bearing faults is paramount to reducing maintenance costs and operational breakdowns.
Class-wise Generalization Error: an Information-Theoretic Analysis
Existing generalization theories of supervised learning typically take a holistic approach and provide bounds for the expected generalization over the whole data distribution, which implicitly assumes that the model generalizes similarly for all the classes.
Efficient Bitrate Ladder Construction using Transfer Learning and Spatio-Temporal Features
Providing high-quality video with efficient bitrate is a main challenge in video industry.
Trustworthiness of $\mathbb{X}$ Users: A One-Class Classification Approach
In this study, we use various OCC models for $\mathbb{X}$ user classification.
Panoramic Image Inpainting With Gated Convolution And Contextual Reconstruction Loss
In response to these challenges, we propose a panoramic image inpainting framework that consists of a Face Generator, a Cube Generator, a side branch, and two discriminators.
Pixel-Wise Color Constancy via Smoothness Techniques in Multi-Illuminant Scenes
Motivated by this, we propose a novel multi-illuminant color constancy method, by learning pixel-wise illumination maps caused by multiple light sources.
Perceptual Learned Image Compression via End-to-End JND-Based Optimization
Emerging Learned image Compression (LC) achieves significant improvements in coding efficiency by end-to-end training of neural networks for compression.
Revisiting Generative Adversarial Networks for Binary Semantic Segmentation on Imbalanced Datasets
In particular, the proposed framework containing a cGANs and a novel auxiliary network is developed to enhance and stabilize the generator's performance under two alternative training stages, when estimating a multiscale probability feature map from heterogeneous and imbalanced inputs iteratively.
Joint End-to-End Image Compression and Denoising: Leveraging Contrastive Learning and Multi-Scale Self-ONNs
Noisy images are a challenge to image compression algorithms due to the inherent difficulty of compressing noise.
Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite Kernel Strategy in One-Class Classification
Early detection of myocardial infarction (MI), a critical condition arising from coronary artery disease (CAD), is vital to prevent further myocardial damage.
Channel-wise Feature Decorrelation for Enhanced Learned Image Compression
The emerging Learned Compression (LC) replaces the traditional codec modules with Deep Neural Networks (DNN), which are trained end-to-end for rate-distortion performance.
MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wild
Within the field of multimodal DFER, recent methods have focused on exploiting advances of self-supervised learning (SSL) for pre-training of strong multimodal encoders.
Ranked #1 on
Dynamic Facial Expression Recognition
on MAFW
Dynamic Facial Expression Recognition
Facial Expression Recognition
+2
