2 code implementations • 1 Dec 2020 • Zahra Atashgahi, Ghada Sokar, Tim Van der Lee, Elena Mocanu, Decebal Constantin Mocanu, Raymond Veldhuis, Mykola Pechenizkiy
This method, named QuickSelection, introduces the strength of the neuron in sparse neural networks as a criterion to measure the feature importance.
1 code implementation • ICCV 2023 • Shunxin Wang, Raymond Veldhuis, Christoph Brune, Nicola Strisciuglio
Our results demonstrate that NNs tend to find simple solutions for classification, and what they learn first during training depends on the most distinctive frequency characteristics, which can be either low- or high-frequencies.
1 code implementation • 8 Jul 2022 • Zahra Atashgahi, Decebal Constantin Mocanu, Raymond Veldhuis, Mykola Pechenizkiy
We show that ALACPD, on average, ranks first among state-of-the-art CPD algorithms in terms of quality of the time series segmentation, and it is on par with the best performer in terms of the accuracy of the estimated change-points.
1 code implementation • 10 Mar 2023 • Zahra Atashgahi, Xuhao Zhang, Neil Kichler, Shiwei Liu, Lu Yin, Mykola Pechenizkiy, Raymond Veldhuis, Decebal Constantin Mocanu
Feature selection that selects an informative subset of variables from data not only enhances the model interpretability and performance but also alleviates the resource demands.
2 code implementations • 17 Mar 2019 • Zahra Atashgahi, Joost Pieterse, Shiwei Liu, Decebal Constantin Mocanu, Raymond Veldhuis, Mykola Pechenizkiy
Concretely, by exploiting the cosine similarity metric to measure the importance of the connections, our proposed method, Cosine similarity-based and Random Topology Exploration (CTRE), evolves the topology of sparse neural networks by adding the most important connections to the network without calculating dense gradient in the backward.
1 code implementation • 28 Jul 2023 • Ioana Mazilu, Shunxin Wang, Sven Dummer, Raymond Veldhuis, Christoph Brune, Nicola Strisciuglio
We train autoencoders with implicit and explicit regularization techniques to enforce linearity relations among the representations of different blur levels in the latent space.
1 code implementation • 12 Aug 2023 • Shunxin Wang, Christoph Brune, Raymond Veldhuis, Nicola Strisciuglio
We propose a data augmentation strategy, named DFM-X, that leverages knowledge about frequency shortcuts, encoded in Dominant Frequencies Maps computed for image classification models.
no code implementations • 24 Oct 2015 • Abhishek Dutta, Raymond Veldhuis, Luuk Spreeuwers
This paper proposes a data driven model to predict the performance of a face recognition system based on image quality features.
no code implementations • 23 Oct 2013 • Abhishek Dutta, Raymond Veldhuis, Luuk Spreeuwers
In Biometrics, facial uniqueness is commonly inferred from impostor similarity scores.
no code implementations • 11 Jun 2020 • Kiran Raja, Matteo Ferrara, Annalisa Franco, Luuk Spreeuwers, Illias Batskos, Florens de Wit Marta Gomez-Barrero, Ulrich Scherhag, Daniel Fischer, Sushma Venkatesh, Jag Mohan Singh, Guoqiang Li, Loïc Bergeron, Sergey Isadskiy, Raghavendra Ramachandra, Christian Rathgeb, Dinusha Frings, Uwe Seidel, Fons Knopjes, Raymond Veldhuis, Davide Maltoni, Christoph Busch
Further, we present a new online evaluation platform to test algorithms on sequestered data.
no code implementations • 19 Jun 2020 • Dan Zeng, Raymond Veldhuis, Luuk Spreeuwers
As a part of this review, we introduce face detection under occlusion, a preliminary step in face recognition.
no code implementations • 30 Nov 2021 • Una M. Kelly, Raymond Veldhuis, Luuk Spreeuwers
Face Recognition (FR) systems have been shown to be vulnerable to morphing attacks.
no code implementations • 7 Feb 2023 • Mahdi Ghafourian, Julian Fierrez, Luis Felipe Gomez, Ruben Vera-Rodriguez, Aythami Morales, Zohra Rezgui, Raymond Veldhuis
The remarkable success of face recognition (FR) has endangered the privacy of internet users particularly in social media.
1 code implementation • 10 May 2023 • Shunxin Wang, Raymond Veldhuis, Christoph Brune, Nicola Strisciuglio
The performance of computer vision models are susceptible to unexpected changes in input images, known as common corruptions (e. g. noise, blur, illumination changes, etc.
no code implementations • 28 May 2023 • Zahra Atashgahi, Mykola Pechenizkiy, Raymond Veldhuis, Decebal Constantin Mocanu
Efficiency in DNNs can be achieved through sparse connectivity and reducing the model size.
no code implementations • 19 Aug 2023 • Marcel Grimmer, Christian Rathgeb, Raymond Veldhuis, Christoph Busch
Accurate face recognition systems are increasingly important in sensitive applications like border control or migration management.
1 code implementation • 18 Mar 2024 • Ahmad Hassanpour, Fatemeh Jamalbafrani, Bian Yang, Kiran Raja, Raymond Veldhuis, Julian Fierrez
We further improve the StyleGAN output to find the optimal code in the latent space using a new optimization for GAN inversion technique.