249 citations · 1.5k across the 70 of their papers we have counts for
20 papers · 1 filter
Classification of Polarimetric SAR Images Using Compact Convolutional Neural Networks
Mete Ahishali, Serkan Kiranyaz, Turker Ince +1
Classification of polarimetric synthetic aperture radar (PolSAR) images is an active research area with a major role in environmental applications. The traditional Machine Learning…
Performance Indicator in Multilinear Compressive Learning
Dat Thanh Tran, Moncef Gabbouj, Alexandros Iosifidis
Recently, the Multilinear Compressive Learning (MCL) framework was proposed to efficiently optimize the sensing and learning steps when working with multidimensional signals, i.e.…
COVID-19 Infection Map Generation and Detection from Chest X-Ray Images
Aysen Degerli, Mete Ahishali, Mehmet Yamac +6
Computer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numer…
Operational vs Convolutional Neural Networks for Image Denoising
Junaid Malik, Serkan Kiranyaz, Moncef Gabbouj
Convolutional Neural Networks (CNNs) have recently become a favored technique for image denoising due to its adaptive learning ability, especially with a deep configuration. Howeve…
Graph Embedding with Data Uncertainty
Firas Laakom, Jenni Raitoharju, Nikolaos Passalis +2
spectral-based subspace learning is a common data preprocessing step in many machine learning pipelines. The main aim is to learn a meaningful low dimensional embedding of the data…
Self-Organized Operational Neural Networks for Severe Image Restoration Problems
Junaid Malik, Serkan Kiranyaz, Moncef Gabbouj
Discriminative learning based on convolutional neural networks (CNNs) aims to perform image restoration by learning from training examples of noisy-clean image pairs. It has become…
Exploiting Heterogeneity in Operational Neural Networks by Synaptic Plasticity
Serkan Kiranyaz, Junaid Malik, Habib Ben Abdallah +3
The recently proposed network model, Operational Neural Networks (ONNs), can generalize the conventional Convolutional Neural Networks (CNNs) that are homogenous only with a linear…
Monte Carlo Dropout Ensembles for Robust Illumination Estimation
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis +2
Computational color constancy is a preprocessing step used in many camera systems. The main aim is to discount the effect of the illumination on the colors in the scene and restore…
FastONN -- Python based open-source GPU implementation for Operational Neural Networks
Junaid Malik, Serkan Kiranyaz, Moncef Gabbouj
Operational Neural Networks (ONNs) have recently been proposed as a special class of artificial neural networks for grid structured data. They enable heterogenous non-linear operat…
Attention-based Neural Bag-of-Features Learning for Sequence Data
Dat Thanh Tran, Nikolaos Passalis, Anastasios Tefas +2
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 releva…
Complexity Analysis Of Next-Generation VVC Encoding and Decoding
Farhad Pakdaman, Mohammad Ali Adelimanesh, Moncef Gabbouj +1
While the next generation video compression standard, Versatile Video Coding (VVC), provides a superior compression efficiency, its computational complexity dramatically increases.…
Probabilistic Color Constancy
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis +3
In this paper, we propose a novel unsupervised color constancy method, called Probabilistic Color Constancy (PCC). We define a framework for estimating the illumination of a scene…
Self-Organized Operational Neural Networks with Generative Neurons
Serkan Kiranyaz, Junaid Malik, Habib Ben Abdallah +3
Operational Neural Networks (ONNs) have recently been proposed to address the well-known limitations and drawbacks of conventional Convolutional Neural Networks (CNNs) such as netw…
Saliency-based Weighted Multi-label Linear Discriminant Analysis
Lei Xu, Jenni Raitoharju, Alexandros Iosifidis +1
In this paper, we propose a new variant of Linear Discriminant Analysis (LDA) to solve multi-label classification tasks. The proposed method is based on a probabilistic model for d…
Convolutional Sparse Support Estimator Network (CSEN) From energy efficient support estimation to learning-aided Compressive Sensing
Mehmet Yamac, Mete Ahishali, Serkan Kiranyaz +1
Support estimation (SE) of a sparse signal refers to finding the location indices of the non-zero elements in a sparse representation. Most of the traditional approaches dealing wi…
Not all domains are equally complex: Adaptive Multi-Domain Learning
Ali Senhaji, Jenni Raitoharju, Moncef Gabbouj +1
Deep learning approaches are highly specialized and require training separate models for different tasks. Multi-domain learning looks at ways to learn a multitude of different task…
Ellipsoidal Subspace Support Vector Data Description
Fahad Sohrab, Jenni Raitoharju, Alexandros Iosifidis +1
In this paper, we propose a novel method for transforming data into a low-dimensional space optimized for one-class classification. The proposed method iteratively transforms data…
Multilinear Compressive Learning with Prior Knowledge
Dat Thanh Tran, Moncef Gabbouj, Alexandros Iosifidis
The recently proposed Multilinear Compressive Learning (MCL) framework combines Multilinear Compressive Sensing and Machine Learning into an end-to-end system that takes into accou…
Incremental Fast Subclass Discriminant Analysis
Kateryna Chumachenko, Jenni Raitoharju, Moncef Gabbouj +1
This paper proposes an incremental solution to Fast Subclass Discriminant Analysis (fastSDA). We present an exact and an approximate linear solution, along with an approximate kern…
Subset Sampling For Progressive Neural Network Learning
Dat Thanh Tran, Moncef Gabbouj, Alexandros Iosifidis
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. While this…