no code implementations • ICML 2020 • Jongseok Lee, Matthias Humt, Jianxiang Feng, Rudolph Triebel
As a result, we show that the information form of MND can be scalably applied to represent model uncertainty in MND.
no code implementations • 11 Nov 2023 • Jianxiang Feng, JongSeok Lee, Simon Geisler, Stephan Gunnemann, Rudolph Triebel
To facilitate reliable deployments of autonomous robots in the real world, Out-of-Distribution (OOD) detection capabilities are often required.
1 code implementation • 15 Jul 2023 • Dominik Schnaus, JongSeok Lee, Daniel Cremers, Rudolph Triebel
In this work, we propose a novel prior learning method for advancing generalization and uncertainty estimation in deep neural networks.
no code implementations • 18 Oct 2022 • JongSeok Lee, Ribin Balachandran, Konstantin Kondak, Andre Coelho, Marco De Stefano, Matthias Humt, Jianxiang Feng, Tamim Asfour, Rudolph Triebel
This article presents a novel telepresence system for advancing aerial manipulation in dynamic and unstructured environments.
no code implementations • 23 Sep 2021 • Jianxiang Feng, JongSeok Lee, Maximilian Durner, Rudolph Triebel
While learning from synthetic training data has recently gained an increased attention, in real-world robotic applications, there are still performance deficiencies due to the so-called Sim-to-Real gap.
no code implementations • 20 Sep 2021 • JongSeok Lee, Jianxiang Feng, Matthias Humt, Marcus G. Müller, Rudolph Triebel
This paper presents a probabilistic framework to obtain both reliable and fast uncertainty estimates for predictions with Deep Neural Networks (DNNs).
no code implementations • IEEE International Conference on Image Processing (ICIP) 2021 • Jaehwan Kim, Soo Min Kang, Kwang Pyo Choi, Youngo Park, Chaeeun Lee, JongSeok Lee
In this paper, an unsupervised image downscaler that preserves the high frequency content of the original image based on an autoencoder is presented.
no code implementations • 7 Jul 2021 • Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, JongSeok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, Muhammad Shahzad, Wen Yang, Richard Bamler, Xiao Xiang Zhu
Different examples from the wide spectrum of challenges in different fields give an idea of the needs and challenges regarding uncertainties in practical applications.
no code implementations • 30 Oct 2020 • Matthias Humt, JongSeok Lee, Rudolph Triebel
In robotics, deep learning (DL) methods are used more and more widely, but their general inability to provide reliable confidence estimates will ultimately lead to fragile and unreliable systems.
no code implementations • 20 Jun 2020 • Jongseok Lee, Matthias Humt, Jianxiang Feng, Rudolph Triebel
We present a sparse representation of model uncertainty for Deep Neural Networks (DNNs) where the parameter posterior is approximated with an inverse formulation of the Multivariate Normal Distribution (MND), also known as the information form.
no code implementations • 25 Mar 2020 • Jongseok Lee, Ribin Balachandran, Yuri S. Sarkisov, Marco De Stefano, Andre Coelho, Kashmira Shinde, Min Jun Kim, Rudolph Triebel, Konstantin Kondak
This paper presents a novel telepresence system for enhancing aerial manipulation capabilities.
no code implementations • 25 Sep 2019 • JongSeok Lee, Rudolph Triebel
This paper addresses the problem of representing a system's belief using multi-variate normal distributions (MND) where the underlying model is based on a deep neural network (DNN).