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

Dynamic Variational Autoencoders for Visual Process Modeling

1 code implementation • 20 Mar 2018 • Alexander Sagel, Hao Shen

This work studies the problem of modeling visual processes by leveraging deep generative architectures for learning linear, Gaussian representations from observed sequences.

Alignment Distances on Systems of Bags

no code implementations • 14 Jun 2017 • Alexander Sagel, Martin Kleinsteuber

Recent research in image and video recognition indicates that many visual processes can be thought of as being generated by a time-varying generative model.

Descriptive Dictionary Learning +2

Texture Retrieval via the Scattering Transform

no code implementations • 12 Jan 2015 • Alexander Sagel, Dominik Meyer, Hao Shen

Our approach employs a recently developed method, the so-called Scattering transform, for the process of feature extraction in texture retrieval.

Content-Based Image Retrieval Retrieval +1