no code implementations • 10 Apr 2024 • Bjørn Leth Møller, Bobby Zhao Sheng Lo, Johan Burisch, Flemming Bendtsen, Ida Vind, Bulat Ibragimov, Christian Igel
We propose using a machine-learning based MES classification system to support the endoscopic process and to mitigate the observer-variability.
1 code implementation • 19 Mar 2024 • Raghavendra Selvan, Bob Pepin, Christian Igel, Gabrielle Samuel, Erik B Dam
The recent advances in deep learning (DL) have been accelerated by access to large-scale data and compute.
no code implementations • 4 Mar 2024 • Lei LI, Tianfang Zhang, Zhongyu Jiang, Cheng-Yen Yang, Jenq-Neng Hwang, Stefan Oehmcke, Dimitri Pierre Johannes Gominski, Fabian Gieseke, Christian Igel
We leverage the fusion of three-dimensional LiDAR measurements and 2D imagery to facilitate the accurate counting of trees.
no code implementations • 20 Nov 2023 • HUI ZHANG, Ankit Kariryaa, Venkanna Babu Guthula, Christian Igel, Stefan Oehmcke
This paper studies how to combine accurate point labels of urban trees along streets with crowd-sourced annotations from an open geographic database to delineate city trees in remote sensing images, a task which is challenging even for humans.
no code implementations • 14 Nov 2023 • Dimitri Gominski, Ankit Kariryaa, Martin Brandt, Christian Igel, Sizhuo Li, Maurice Mugabowindekwe, Rasmus Fensholt
There is a rising interest in mapping trees using satellite or aerial imagery, but there is no standardized evaluation protocol for comparing and enhancing methods.
no code implementations • 8 Nov 2023 • Philip Enevoldsen, Christian Gundersen, Nico Lang, Serge Belongie, Christian Igel
Open-set recognition (OSR), the identification of novel categories, can be a critical component when deploying classification models in real-world applications.
no code implementations • 5 Sep 2023 • Dustin Wright, Christian Igel, Gabrielle Samuel, Raghavendra Selvan
The solution lionized by both industry and the ML community to improve the environmental sustainability of ML is to increase the efficiency with which ML systems operate in terms of both compute and energy consumption.
1 code implementation • 7 Jul 2023 • Matthias Freiberger, Peter Kun, Christian Igel, Anders Sundnes Løvlie, Sebastian Risi
We investigate the properties of the mined images, and find that images trained on a small number of image captions generalize to a much larger number of semantically related captions.
no code implementations • 1 Jun 2023 • Christian Igel
The resulting smooth min-max (SMM) network module inherits the asymptotic approximation properties from the MM architecture.
no code implementations • 15 Jan 2023 • Lei LI, Tianfang Zhang, Stefan Oehmcke, Fabian Gieseke, Christian Igel
Building segmentation from aerial images and 3D laser scanning (LiDAR) is a challenging task due to the diversity of backgrounds, building textures, and image quality.
no code implementations • 18 Dec 2022 • Tianfang Zhang, Lei LI, Christian Igel, Stefan Oehmcke, Fabian Gieseke, Zhenming Peng
In this work, we propose a DUN called low-rank CS network (LR-CSNet) for natural image CS.
2 code implementations • 12 Oct 2022 • Pedram Bakhtiarifard, Christian Igel, Raghavendra Selvan
We advocate for including energy efficiency as an additional performance criterion in NAS.
no code implementations • 21 May 2022 • Abdelrahman Mohamed, Hung-Yi Lee, Lasse Borgholt, Jakob D. Havtorn, Joakim Edin, Christian Igel, Katrin Kirchhoff, Shang-Wen Li, Karen Livescu, Lars Maaløe, Tara N. Sainath, Shinji Watanabe
Although self-supervised speech representation is still a nascent research area, it is closely related to acoustic word embedding and learning with zero lexical resources, both of which have seen active research for many years.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 30 Mar 2022 • Christian Igel, Stefan Oehmcke
We suggest to adjust the bias of the machine learning model after training as a default postprocessing step, which efficiently solves the problem.
no code implementations • 1 Mar 2022 • Lasse Borgholt, Jakob Drachmann Havtorn, Joakim Edin, Lars Maaløe, Christian Igel
Unsupervised representation learning for speech processing has matured greatly in the last few years.
no code implementations • 21 Dec 2021 • Stefan Oehmcke, Lei LI, Katerina Trepekli, Jaime Revenga, Thomas Nord-Larsen, Fabian Gieseke, Christian Igel
Quantification of forest biomass stocks and their dynamics is important for implementing effective climate change mitigation measures.
no code implementations • 29 Nov 2021 • Lasse Borgholt, Jakob Drachmann Havtorn, Mostafa Abdou, Joakim Edin, Lars Maaløe, Anders Søgaard, Christian Igel
We compare learned speech features from wav2vec 2. 0, state-of-the-art ASR transcripts, and the ground truth text as input for a novel speech-based named entity recognition task, a cardiac arrest detection task on real-world emergency calls and two existing SLU benchmarks.
Ranked #7 on Spoken Language Understanding on Fluent Speech Commands (using extra training data)
Automatic Speech Recognition Automatic Speech Recognition (ASR) +8
1 code implementation • NeurIPS 2021 • Yi-Shan Wu, Andrés R. Masegosa, Stephan S. Lorenzen, Christian Igel, Yevgeny Seldin
The bound is based on a novel parametric form of the Chebyshev- Cantelli inequality (a. k. a.
1 code implementation • ICLR 2022 • Stephan Sloth Lorenzen, Christian Igel, Mads Nielsen
In this setting, we observed a fitting phase for all layers and a compression phase for the output layer in all experiments; the compression in the hidden layers was dependent on the type of activation function.
no code implementations • 17 Feb 2021 • Lasse Borgholt, Jakob Drachmann Havtorn, Željko Agić, Anders Søgaard, Lars Maaløe, Christian Igel
We test this hypothesis by measuring temporal context sensitivity and evaluate how the models perform when we constrain the amount of contextual information in the audio input.
no code implementations • 1 Feb 2021 • Lasse Borgholt, Tycho Max Sylvester Tax, Jakob Drachmann Havtorn, Lars Maaløe, Christian Igel
We explore the performance of such systems without fine-tuning by training a state-of-the-art speech recognizer on the fixed representations from the computationally demanding wav2vec 2. 0 framework.
1 code implementation • 18 Jan 2021 • Svetlana Kutuzova, Oswin Krause, Douglas McCloskey, Mads Nielsen, Christian Igel
Multimodal generative models should be able to learn a meaningful latent representation that enables a coherent joint generation of all modalities (e. g., images and text).
1 code implementation • NeurIPS 2020 • Steffen Czolbe, Oswin Krause, Ingemar Cox, Christian Igel
To train Variational Autoencoders (VAEs) to generate realistic imagery requires a loss function that reflects human perception of image similarity.
1 code implementation • NeurIPS 2020 • Andrés R. Masegosa, Stephan S. Lorenzen, Christian Igel, Yevgeny Seldin
We present a novel analysis of the expected risk of weighted majority vote in multiclass classification.
no code implementations • 26 Jun 2020 • Kai Brügge, Asja Fischer, Christian Igel
We propose a modified Metropolis transition operator that behaves almost always identically to the standard Metropolis operator and prove that it ensures irreducibility and convergence to the limiting distribution in the multivariate binary case with fixed-order updates.
1 code implementation • 26 Jun 2020 • Steffen Czolbe, Oswin Krause, Ingemar Cox, Christian Igel
To train Variational Autoencoders (VAEs) to generate realistic imagery requires a loss function that reflects human perception of image similarity.
2 code implementations • 29 Apr 2020 • Arjun D. Desai, Francesco Caliva, Claudia Iriondo, Naji Khosravan, Aliasghar Mortazi, Sachin Jambawalikar, Drew Torigian, Jutta Ellermann, Mehmet Akcakaya, Ulas Bagci, Radhika Tibrewala, Io Flament, Matthew O`Brien, Sharmila Majumdar, Mathias Perslev, Akshay Pai, Christian Igel, Erik B. Dam, Sibaji Gaj, Mingrui Yang, Kunio Nakamura, Xiaojuan Li, Cem M. Deniz, Vladimir Juras, Ravinder Regatte, Garry E. Gold, Brian A. Hargreaves, Valentina Pedoia, Akshay S. Chaudhari
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression.
no code implementations • ECCV 2020 • Urun Dogan, Aniket Anand Deshmukh, Marcin Machura, Christian Igel
We propose a novel curriculum learning approach for image classification that adapts the loss function by changing the label representation.
2 code implementations • 5 Nov 2019 • Mathias Perslev, Erik Bjørnager Dam, Akshay Pai, Christian Igel
The system relies on multi-planar data augmentation which facilitates the application of a single 2D architecture based on the familiar U-Net.
5 code implementations • NeurIPS 2019 • Mathias Perslev, Michael Hejselbak Jensen, Sune Darkner, Poul Jørgen Jennum, Christian Igel
We propose U-Time, a fully feed-forward deep learning approach to physiological time series segmentation developed for the analysis of sleep data.
no code implementations • 16 Aug 2019 • Mauricio Orbes-Arteaga, Jorge Cardoso, Lauge Sørensen, Christian Igel, Sebastien Ourselin, Marc Modat, Mads Nielsen, Akshay Pai
As a result, their performance is significantly lower on data from unseen sources compared to the performance on data from the same source as the training data.
6 code implementations • 13 Jan 2019 • Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, Fabian Lohöfer, Julian Walter Holch, Wieland Sommer, Felix Hofmann, Alexandre Hostettler, Naama Lev-Cohain, Michal Drozdzal, Michal Marianne Amitai, Refael Vivantik, Jacob Sosna, Ivan Ezhov, Anjany Sekuboyina, Fernando Navarro, Florian Kofler, Johannes C. Paetzold, Suprosanna Shit, Xiaobin Hu, Jana Lipková, Markus Rempfler, Marie Piraud, Jan Kirschke, Benedikt Wiestler, Zhiheng Zhang, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Michela Antonelli, Woong Bae, Míriam Bellver, Lei Bi, Hao Chen, Grzegorz Chlebus, Erik B. Dam, Qi Dou, Chi-Wing Fu, Bogdan Georgescu, Xavier Giró-i-Nieto, Felix Gruen, Xu Han, Pheng-Ann Heng, Jürgen Hesser, Jan Hendrik Moltz, Christian Igel, Fabian Isensee, Paul Jäger, Fucang Jia, Krishna Chaitanya Kaluva, Mahendra Khened, Ildoo Kim, Jae-Hun Kim, Sungwoong Kim, Simon Kohl, Tomasz Konopczynski, Avinash Kori, Ganapathy Krishnamurthi, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Li, John Lowengrub, Jun Ma, Klaus Maier-Hein, Kevis-Kokitsi Maninis, Hans Meine, Dorit Merhof, Akshay Pai, Mathias Perslev, Jens Petersen, Jordi Pont-Tuset, Jin Qi, Xiaojuan Qi, Oliver Rippel, Karsten Roth, Ignacio Sarasua, Andrea Schenk, Zengming Shen, Jordi Torres, Christian Wachinger, Chunliang Wang, Leon Weninger, Jianrong Wu, Daguang Xu, Xiaoping Yang, Simon Chun-Ho Yu, Yading Yuan, Miao Yu, Liping Zhang, Jorge Cardoso, Spyridon Bakas, Rickmer Braren, Volker Heinemann, Christopher Pal, An Tang, Samuel Kadoury, Luc Soler, Bram van Ginneken, Hayit Greenspan, Leo Joskowicz, Bjoern Menze
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
1 code implementation • 23 Oct 2018 • Stephan Sloth Lorenzen, Christian Igel, Yevgeny Seldin
This effect provides a significant boost in performance when the errors are independent or negatively correlated, but when the correlations are strong the advantage from taking the majority vote is small.
no code implementations • 3 Oct 2018 • Mauricio Orbes Arteaga, Lauge Sørensen, M. Jorge Cardoso, Marc Modat, Sebastien Ourselin, Stefan Sommer, Mads Nielsen, Christian Igel, Akshay Pai
For proper generalization performance of convolutional neural networks (CNNs) in medical image segmentation, the learnt features should be invariant under particular non-linear shape variations of the input.
1 code implementation • 18 Feb 2018 • Fabian Gieseke, Christian Igel
Without access to large compute clusters, building random forests on large datasets is still a challenging problem.
no code implementations • 15 Apr 2017 • Jan Kremer, Kristoffer Stensbo-Smidt, Fabian Gieseke, Kim Steenstrup Pedersen, Christian Igel
Astrophysics and cosmology are rich with data.
no code implementations • NeurIPS 2016 • Oswin Krause, Dídac Rodríguez Arbonès, Christian Igel
The covariance matrix adaptation evolution strategy (CMA-ES) is arguably one of the most powerful real-valued derivative-free optimization algorithms, finding many applications in machine learning.
no code implementations • 19 Aug 2016 • Niklas Thiemann, Christian Igel, Olivier Wintenberger, Yevgeny Seldin
We propose a new PAC-Bayesian bound and a way of constructing a hypothesis space, so that the bound is convex in the posterior distribution and also convex in a trade-off parameter between empirical performance of the posterior distribution and its complexity.
1 code implementation • 9 Dec 2015 • Fabian Gieseke, Cosmin Eugen Oancea, Ashish Mahabal, Christian Igel, Tom Heskes
A buffer k-d tree is a k-d tree variant for massively-parallel nearest neighbor search.
1 code implementation • 17 Nov 2015 • Kristoffer Stensbo-Smidt, Fabian Gieseke, Christian Igel, Andrew Zirm, Kim Steenstrup Pedersen
This study promotes a feature selection algorithm, which selects the most informative magnitudes and colours for a given task of estimating physical quantities from photometric data alone.
no code implementations • 6 Oct 2015 • Oswin Krause, Asja Fischer, Christian Igel
Compared to CD, it leads to a consistent estimate and may have a significantly lower bias.