249 citations · 1.5k across the 70 of their papers we have counts for
86 papers
CASR: Content-Adaptive Neural Super-Resolution Post-Filter for Versatile Video Coding via Low-Rank Overfitting
Khoa Pham-Dinh, Francesco Cricri, Maria Santamaria +5
The use of super-resolution as a post-processing step following a video codec allows videos to be encoded at reduced spatial resolution at the encoder side and to be reconstructed…
Credit Card Fraud Detection with Subspace Learning-based One-Class Classification
Zaffar Zaffar, Fahad Sohrab, Juho Kanniainen +1
In an increasingly digitalized commerce landscape, the proliferation of credit card fraud and the evolution of sophisticated fraudulent techniques have led to substantial financial…
One-Class Classification for Intrusion Detection on Vehicular Networks
Jake Guidry, Fahad Sohrab, Raju Gottumukkala +2
Controller Area Network bus systems within vehicular networks are not equipped with the tools necessary to ward off and protect themselves from modern cyber-security threats. Work…
Newton Method-based Subspace Support Vector Data Description
Fahad Sohrab, Firas Laakom, Moncef Gabbouj
In this paper, we present an adaptation of Newton's method for the optimization of Subspace Support Vector Data Description (S-SVDD). The objective of S-SVDD is to map the original…
Comprehensive Complexity Assessment of Emerging Learned Image Compression on CPU and GPU
Farhad Pakdaman, Moncef Gabbouj
Learned Compression (LC) is the emerging technology for compressing image and video content, using deep neural networks. Despite being new, LC methods have already gained a compres…
End-to-end Transformer for Compressed Video Quality Enhancement
Li Yu, Wenshuai Chang, Shiyu Wu +1
Convolutional neural networks have achieved excellent results in compressed video quality enhancement task in recent years. State-of-the-art methods explore the spatiotemporal info…
Efficient CNN with uncorrelated Bag of Features pooling
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis +1
Despite the superior performance of CNN, deploying them on low computational power devices is still limited as they are typically computationally expensive. One key cause of the hi…
A Survey on Mobile Edge Computing for Video Streaming: Opportunities and Challenges
Muhammad Asif Khan, Emna Baccour, Zina Chkirbene +4
5G communication brings substantial improvements in the quality of service provided to various applications by achieving higher throughput and lower latency. However, interactive m…
Reducing Redundancy in the Bottleneck Representation of the Autoencoders
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis +1
Autoencoders are a type of unsupervised neural networks, which can be used to solve various tasks, e.g., dimensionality reduction, image compression, and image denoising. An AE has…
Image denoising by Super Neurons: Why go deep?
Junaid Malik, Serkan Kiranyaz, Moncef Gabbouj
Classical image denoising methods utilize the non-local self-similarity principle to effectively recover image content from noisy images. Current state-of-the-art methods use deep…
Learning to ignore: rethinking attention in CNNs
Firas Laakom, Kateryna Chumachenko, Jenni Raitoharju +2
Recently, there has been an increasing interest in applying attention mechanisms in Convolutional Neural Networks (CNNs) to solve computer vision tasks. Most of these methods learn…
Robust channel-wise illumination estimation
Firas Laakom, Jenni Raitoharju, Jarno Nikkanen +2
Recently, Convolutional Neural Networks (CNNs) have been widely used to solve the illuminant estimation problem and have often led to state-of-the-art results. Standard approaches…
Early Myocardial Infarction Detection over Multi-view Echocardiography
Aysen Degerli, Serkan Kiranyaz, Tahir Hamid +2
Myocardial infarction (MI) is the leading cause of mortality in the world that occurs due to a blockage of the coronary arteries feeding the myocardium. An early diagnosis of MI an…
Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks
Moncef Gabbouj, Serkan Kiranyaz, Junaid Malik +5
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 acqu…
Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks
Junaid Malik, Ozer Can Devecioglu, Serkan Kiranyaz +2
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 ac…
Real-Time Glaucoma Detection from Digital Fundus Images using Self-ONNs
Ozer Can Devecioglu, Junaid Malik, Turker Ince +3
Glaucoma leads to permanent vision disability by damaging the optical nerve that transmits visual images to the brain. The fact that glaucoma does not show any symptoms as it progr…
Remote Multilinear Compressive Learning with Adaptive Compression
Dat Thanh Tran, Moncef Gabbouj, Alexandros Iosifidis
Multilinear Compressive Learning (MCL) is an efficient signal acquisition and learning paradigm for multidimensional signals. The level of signal compression affects the detection…
Super Neurons
Serkan Kiranyaz, Junaid Malik, Mehmet Yamac +5
Self-Organized Operational Neural Networks (Self-ONNs) have recently been proposed as new-generation neural network models with nonlinear learning units, i.e., the generative neuro…
Bilinear Input Normalization for Neural Networks in Financial Forecasting
Dat Thanh Tran, Juho Kanniainen, Moncef Gabbouj +1
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. This is…
Generalized Tensor Summation Compressive Sensing Network (GTSNET): An Easy to Learn Compressive Sensing Operation
Mehmet Yamac, Ugur Akpinar, Erdem Sahin +2
In CS literature, the efforts can be divided into two groups: finding a measurement matrix that preserves the compressed information at the maximum level, and finding a reconstruct…
Representation Based Regression for Object Distance Estimation
Mete Ahishali, Mehmet Yamac, Serkan Kiranyaz +1
In this study, we propose a novel approach to predict the distances of the detected objects in an observed scene. The proposed approach modifies the recently proposed Convolutional…
Graph-Embedded Subspace Support Vector Data Description
Fahad Sohrab, Alexandros Iosifidis, Moncef Gabbouj +1
In this paper, we propose a novel subspace learning framework for one-class classification. The proposed framework presents the problem in the form of graph embedding. It includes…
BM3D vs 2-Layer ONN
Junaid Malik, Serkan Kiranyaz, Mehmet Yamac +1
Despite their recent success on image denoising, the need for deep and complex architectures still hinders the practical usage of CNNs. Older but computationally more efficient met…
Convolutional versus Self-Organized Operational Neural Networks for Real-World Blind Image Denoising
Junaid Malik, Serkan Kiranyaz, Mehmet Yamac +2
Real-world blind denoising poses a unique image restoration challenge due to the non-deterministic nature of the underlying noise distribution. Prevalent discriminative networks tr…