54 citations · 66 across the 16 of their papers we have counts for
16 papers
Computer vision-based estimation of invertebrate biomass
Mikko Impiö, Philipp M. Rehsen, Jarrett Blair +5
The ability to estimate invertebrate biomass using only images could help scaling up quantitative biodiversity monitoring efforts. Computer vision-based methods have the potential…
ULW-SleepNet: An Ultra-Lightweight Network for Multimodal Sleep Stage Scoring
Zhaowen Wang, Dongdong Zhou, Qi Xu +3
Automatic sleep stage scoring is crucial for the diagnosis and treatment of sleep disorders. Although deep learning models have advanced the field, many existing models are computa…
Generalized Reference Kernel With Negative Samples For Support Vector One-class Classification
Jenni Raitoharju
This paper focuses on small-scale one-class classification with some negative samples available. We propose Generalized Reference Kernel with Negative Samples (GRKneg) for One-clas…
Linear-time One-Class Classification with Repeated Element-wise Folding
Jenni Raitoharju
This paper proposes an easy-to-use method for one-class classification: Repeated Element-wise Folding (REF). The algorithm consists of repeatedly standardizing and applying an elem…
Convolutional autoencoder-based multimodal one-class classification
Firas Laakom, Fahad Sohrab, Jenni Raitoharju +2
One-class classification refers to approaches of learning using data from a single class only. In this paper, we propose a deep learning one-class classification method suitable fo…
On Feature Diversity in Energy-based Models
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis +1
Energy-based learning is a powerful learning paradigm that encapsulates various discriminative and generative approaches. An energy-based model (EBM) is typically formed of inner-m…
WLD-Reg: A Data-dependent Within-layer Diversity Regularizer
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis +1
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 t…
Computer Vision on X-ray Data in Industrial Production and Security Applications: A Comprehensive Survey
Mehdi Rafiei, Jenni Raitoharju, Alexandros Iosifidis
X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other a…
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…
Generalized Reference Kernel for One-class Classification
Jenni Raitoharju, Alexandros Iosifidis
In this paper, we formulate a new generalized reference kernel hoping to improve the original base kernel using a set of reference vectors. Depending on the selected reference vect…
Automatic Image Content Extraction: Operationalizing Machine Learning in Humanistic Photographic Studies of Large Visual Archives
Anssi Männistö, Mert Seker, Alexandros Iosifidis +1
Applying machine learning tools to digitized image archives has a potential to revolutionize quantitative research of visual studies in humanities and social sciences. The ability…
Non-Linear Spectral Dimensionality Reduction Under Uncertainty
Firas Laakom, Jenni Raitoharju, Nikolaos Passalis +2
In this paper, we consider the problem of non-linear dimensionality reduction under uncertainty, both from a theoretical and algorithmic perspectives. Since real-world data usually…
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…
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…
Learning distinct features helps, provably
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis +1
We study the diversity of the features learned by a two-layer neural network trained with the least squares loss. We measure the diversity by the average -distance between the…