Lifelong Learning

Team

Daphne Auer, Julia Böhlke, Niklas Penzel, Paul Bodesheim

Motivation

Lifelong learning denotes a concept for continuous learning from data streams that typically become available during the application of a recognition system, e.g., additional images captured during a visual monitoring study. Instead of only applying a fixed model pre-trained on an initial dataset, lifelong learning aims at leveraging the additional data collected during an application in order to improve the recognition model over time. It incorporates active learning to exploit human feedback via annotations of unlabeled samples that have an impact on model training. Furthermore, incremental learning is required to update model parameters effectively and efficiently. In unconstrained environments and open-world scenarios, novelty detection also needs to be considered.

Publications

2019
Information-Theoretic Active Learning for Content-Based Image Retrieval
Björn Barz and Christoph Käding and Joachim Denzler.
German Conference on Pattern Recognition (GCPR) 2018. Lecture Notes in Computer Science. Pages 650-666. 2019.
[bibtex] [pdf] [code] [supplementary] [abstract]
2017
Large-Scale Gaussian Process Inference with Generalized Histogram Intersection Kernels for Visual Recognition Tasks
Erik Rodner and Alexander Freytag and Paul Bodesheim and Björn Fröhlich and Joachim Denzler.
International Journal of Computer Vision (IJCV). 121 (2) : pp. 253-280. 2017.
[bibtex] [pdf] [web] [abstract]
Underwater and in the Cloud: Web-based Machine Learning for Fish Video Analysis
Jonas Jäger and Claudia Kruschel and Stewart T. Schultz and Dubravko Pedjo and Viviane Wolff and Klaus Fricke-Neuderth and Joachim Denzler.
52nd European Marine Biology Symposium. 2017.
[bibtex] [pdf]
2016
Lifelong Learning for Visual Recognition Systems
Alexander Freytag. 2016. ISBN 9783843929950
[bibtex] [pdf] [web]
Large-scale Active Learning with Approximated Expected Model Output Changes
Christoph Käding and Alexander Freytag and Erik Rodner and Andrea Perino and Joachim Denzler.
German Conference on Pattern Recognition (GCPR). Pages 179-191. 2016.
[bibtex] [pdf] [web] [code] [supplementary] [abstract]
Active and Continuous Exploration with Deep Neural Networks and Expected Model Output Changes
Christoph Käding and Erik Rodner and Alexander Freytag and Joachim Denzler.
NIPS Workshop on Continual Learning and Deep Networks (NIPS-WS). 2016.
[bibtex] [pdf] [web] [abstract]
Fine-tuning Deep Neural Networks in Continuous Learning Scenarios
Christoph Käding and Erik Rodner and Alexander Freytag and Joachim Denzler.
ACCV Workshop on Interpretation and Visualization of Deep Neural Nets (ACCV-WS). 2016.
[bibtex] [pdf] [web] [supplementary] [abstract]
Watch, Ask, Learn, and Improve: A Lifelong Learning Cycle for Visual Recognition
Christoph Käding and Erik Rodner and Alexander Freytag and Joachim Denzler.
European Symposium on Artificial Neural Networks (ESANN). Pages 381-386. 2016.
[bibtex] [pdf] [code] [presentation] [abstract]
2015
Active Learning and Discovery of Object Categories in the Presence of Unnameable Instances
Christoph Käding and Alexander Freytag and Erik Rodner and Paul Bodesheim and Joachim Denzler.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Pages 4343-4352. 2015.
[bibtex] [pdf] [web] [code] [presentation] [supplementary] [abstract]
Local Novelty Detection in Multi-class Recognition Problems
Paul Bodesheim and Alexander Freytag and Erik Rodner and Joachim Denzler.
IEEE Winter Conference on Applications of Computer Vision (WACV). Pages 813-820. 2015.
[bibtex] [pdf] [web] [supplementary] [abstract]
2014
Birds of a Feather Flock Together - Local Learning of Mid-level Representations for Fine-grained Recognition
Alexander Freytag and Erik Rodner and Joachim Denzler.
ECCV Workshop on Parts and Attributes (ECCV-WS). 2014.
[bibtex] [pdf] [web] [code] [presentation]
Selecting Influential Examples: Active Learning with Expected Model Output Changes
Alexander Freytag and Erik Rodner and Joachim Denzler.
European Conference on Computer Vision (ECCV). Pages 562-577. 2014.
[bibtex] [pdf] [presentation] [supplementary] [abstract]
Exemplar-specific Patch Features for Fine-grained Recognition
Alexander Freytag and Erik Rodner and Trevor Darrell and Joachim Denzler.
German Conference on Pattern Recognition (GCPR). Pages 144-156. 2014.
[bibtex] [pdf] [code] [supplementary] [abstract]
ARTOS -- Adaptive Real-Time Object Detection System
Björn Barz and Erik Rodner and Joachim Denzler.
arXiv preprint arXiv:1407.2721. 2014.
[bibtex] [pdf] [web] [code] [abstract]
Interactive Adaptation of Real-Time Object Detectors
Daniel Göhring and Judy Hoffman and Erik Rodner and Kate Saenko and Trevor Darrell.
International Conference on Robotics and Automation (ICRA). Pages 1282-1289. 2014.
[bibtex] [pdf] [web] [abstract]
Asymmetric and Category Invariant Feature Transformations for Domain Adaptation
Judy Hoffman and Erik Rodner and Jeff Donahue and Brian Kulis and Kate Saenko.
International Journal of Computer Vision (IJCV). 109 (1-2) : pp. 28-41. 2014.
[bibtex] [pdf] [web] [abstract]
Open-vocabulary Object Retrieval
Sergio Guadarrama and Erik Rodner and Kate Saenko and Ning Zhang and Ryan Farrell and Jeff Donahue and Trevor Darrell.
Robotics Science and Systems (RSS). Pages 41, ISBN 978-0-9923747-0-9. 2014. Awarded with an AAAI invited talk
[bibtex] [pdf] [web] [abstract]
2013
Labeling examples that matter: Relevance-Based Active Learning with Gaussian Processes
Alexander Freytag and Erik Rodner and Paul Bodesheim and Joachim Denzler.
German Conference on Pattern Recognition (GCPR). Pages 282-291. 2013.
[bibtex] [pdf] [web] [code] [supplementary] [abstract]
I Want To Know More: Efficient Multi-Class Incremental Learning Using Gaussian Processes
Alexander Lütz and Erik Rodner and Joachim Denzler.
Pattern Recognition and Image Analysis. Advances in Mathematical Theory and Applications (PRIA). 23 (3) : pp. 402-407. 2013.
[bibtex] [pdf]
Scalable Transform-based Domain Adaptation
Erik Rodner and Judy Hoffman and Jeff Donahue and Trevor Darrell and Kate Saenko.
ICCV Workshop on Visual Domain Adaptation (ICCV-WS). 2013.
[bibtex] [pdf]
Towards Adapting ImageNet to Reality: Scalable Domain Adaptation with Implicit Low-rank Transformations
Erik Rodner and Judy Hoffman and Jeff Donahue and Trevor Darrell and Kate Saenko.
arXiv preprint arXiv:1308.4200. 2013.
[bibtex] [pdf]
Transform-based Domain Adaptation for Big Data
Erik Rodner and Judy Hoffman and Jeff Donahue and Trevor Darrell and Kate Saenko.
NIPS Workshop on New Directions in Transfer and Multi-Task Learning (NIPS-WS). 2013. abstract version of arXiv:1308.4200
[bibtex] [pdf] [abstract]
Semi-Supervised Domain Adaptation with Instance Constraints
Jeff Donahue and Judy Hoffman and Erik Rodner and Kate Saenko and Trevor Darrell.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Pages 668-675. 2013.
[bibtex] [pdf]
Beyond the closed-world assumption: The importance of novelty detection and open set recognition
Joachim Denzler and Erik Rodner and Paul Bodesheim and Alexander Freytag.
GCPR/DAGM Workshop on Unsolved Problems in Pattern Recognition and Computer Vision (GCPR-WS): Extended Abstract + Oral Presentation. 2013.
[bibtex] [pdf] [web]
Efficient Learning of Domain-invariant Image Representations
Judy Hoffman and Erik Rodner and Jeff Donahue and Trevor Darrell and Kate Saenko.
International Conference on Learning Representations (ICLR). 2013.
[bibtex] [pdf]
One-class Classification with Gaussian Processes
Michael Kemmler and Erik Rodner and Esther-Sabrina Wacker and Joachim Denzler.
Pattern Recognition. 46 : pp. 3507-3518. 2013.
[bibtex] [pdf]
An Efficient Approximation for Gaussian Process Regression
Paul Bodesheim and Alexander Freytag and Erik Rodner and Joachim Denzler. 2013. Technical Report TR-FSU-INF-CV-2013-01
[bibtex] [pdf]
Approximations of Gaussian Process Uncertainties for Visual Recognition Problems
Paul Bodesheim and Alexander Freytag and Erik Rodner and Joachim Denzler.
Scandinavian Conference on Image Analysis (SCIA). Pages 182-194. 2013.
[bibtex] [pdf] [web] [abstract]
Kernel Null Space Methods for Novelty Detection
Paul Bodesheim and Alexander Freytag and Erik Rodner and Michael Kemmler and Joachim Denzler.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Pages 3374-3381. 2013.
[bibtex] [pdf] [web] [code] [presentation] [abstract]
2012
Lernen mit wenigen Beispielen für die visuelle Objekterkennung
Erik Rodner.
Ausgezeichnete Informatikdissertationen 2011. 2012. in german
[bibtex] [pdf] [web]
Divergence-Based One-Class Classification Using Gaussian Processes
Paul Bodesheim and Erik Rodner and Alexander Freytag and Joachim Denzler.
British Machine Vision Conference (BMVC). Pages 50.1-50.11. 2012.
[bibtex] [pdf] [web] [presentation] [abstract]
2011
Efficient Multi-Class Incremental Learning Using Gaussian Processes
Alexander Lütz and Erik Rodner and Joachim Denzler.
Open German-Russian Workshop on Pattern Recognition and Image Understanding (OGRW). Pages 182-185. 2011.
[bibtex] [pdf] [abstract]
Learning with Few Examples for Binary and Multiclass Classification Using Regularization of Randomized Trees
Erik Rodner and Joachim Denzler.
Pattern Recognition Letters. 32 (2) : pp. 244-251. 2011.
[bibtex] [pdf]
2010
One-Shot Learning of Object Categories using Dependent Gaussian Processes
Erik Rodner and Joachim Denzler.
Annual Symposium of the German Association for Pattern Recognition (DAGM). Pages 232-241. 2010.
[bibtex] [pdf]
One-Class Classification with Gaussian Processes
Michael Kemmler and Erik Rodner and Joachim Denzler.
Asian Conference on Computer Vision (ACCV). Pages 489-500. 2010.
[bibtex] [pdf] [presentation]
2009
Learning with Few Examples by Transferring Feature Relevance
Erik Rodner and Joachim Denzler.
Annual Symposium of the German Association for Pattern Recognition (DAGM). Pages 252-261. 2009.
[bibtex] [pdf]
2008
Learning with Few Examples using a Constrained Gaussian Prior on Randomized Trees
Erik Rodner and Joachim Denzler.
Vision, Modelling, and Visualization Workshop (VMV). Pages 159-168. 2008.
[bibtex] [pdf]