Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

no code implementations • 10 May 2022 • Julian Wörmann, Daniel Bogdoll, Christian Brunner, Etienne Bührle, Han Chen, Evaristus Fuh Chuo, Kostadin Cvejoski, Ludger van Elst, Philip Gottschall, Stefan Griesche, Christian Hellert, Christian Hesels, Sebastian Houben, Tim Joseph, Niklas Keil, Johann Kelsch, Mert Keser, Hendrik Königshof, Erwin Kraft, Leonie Kreuser, Kevin Krone, Tobias Latka, Denny Mattern, Stefan Matthes, Franz Motzkus, Mohsin Munir, Moritz Nekolla, Adrian Paschke, Stefan Pilar von Pilchau, Maximilian Alexander Pintz, Tianming Qiu, Faraz Qureishi, Syed Tahseen Raza Rizvi, Jörg Reichardt, Laura von Rueden, Alexander Sagel, Diogo Sasdelli, Tobias Scholl, Gerhard Schunk, Gesina Schwalbe, Hao Shen, Youssef Shoeb, Hendrik Stapelbroek, Vera Stehr, Gurucharan Srinivas, Anh Tuan Tran, Abhishek Vivekanandan, Ya Wang, Florian Wasserrab, Tino Werner, Christian Wirth, Stefan Zwicklbauer

The availability of representative datasets is an essential prerequisite for many successful artificial intelligence and machine learning models.

Autonomous Driving BIG-bench Machine Learning

Dynamic Texture Recognition via Nuclear Distances on Kernelized Scattering Histogram Spaces

1 code implementation • 1 Feb 2021 • Alexander Sagel, Julian Wörmann, Hao Shen

Distance-based dynamic texture recognition is an important research field in multimedia processing with applications ranging from retrieval to segmentation of video data.

Classification Dynamic Texture Recognition +2

Knowledge as Invariance -- History and Perspectives of Knowledge-augmented Machine Learning

no code implementations • 21 Dec 2020 • Alexander Sagel, Amit Sahu, Stefan Matthes, Holger Pfeifer, Tianming Qiu, Harald Rueß, Hao Shen, Julian Wörmann

This white paper provides an introduction and discussion of this emerging field in machine learning research.

BIG-bench Machine Learning Data Augmentation

Learning Co-Sparse Analysis Operators with Separable Structures

no code implementations • 9 Mar 2015 • Matthias Seibert, Julian Wörmann, Rémi Gribonval, Martin Kleinsteuber

In many applications, it is also required that the filter responses are obtained in a timely manner, which can be achieved by filters with a separable structure.

Separable Cosparse Analysis Operator Learning

no code implementations • 6 Jun 2014 • Matthias Seibert, Julian Wörmann, Rémi Gribonval, Martin Kleinsteuber

The ability of having a sparse representation for a certain class of signals has many applications in data analysis, image processing, and other research fields.

Operator learning