Incremental Learning

Team
Daphne Auer, Niklas Penzel, Paul Bodesheim
Motivation

Incremental learning refers to updating the parameters of a recognition model with additional (labeled) training data instead of training a new model from scratch with the extended set of training samples. It is also called continuous learning and is highly relevant for applications in which a stream of incoming data (structured in data chunks called experiences) is available and should be exploited for further improving the trained models. It is an important aspect for lifelong learning.

Publications

2017
Erik Rodner, Alexander Freytag, Paul Bodesheim, Björn Fröhlich, Joachim Denzler:
Large-Scale Gaussian Process Inference with Generalized Histogram Intersection Kernels for Visual Recognition Tasks.
International Journal of Computer Vision (IJCV). 121 (2) : pp. 253-280. 2017.
[bibtex] [pdf] [web] [doi] [abstract]
2016
Alexander Freytag:
Lifelong Learning for Visual Recognition Systems.
2016. ISBN 9783843929950
[bibtex] [pdf] [web]
Christoph Käding, Erik Rodner, Alexander Freytag, Joachim Denzler:
Fine-tuning Deep Neural Networks in Continuous Learning Scenarios.
ACCV Workshop on Interpretation and Visualization of Deep Neural Nets (ACCV-WS). 2016.
[bibtex] [pdf] [web] [supplementary] [abstract]
2013
Alexander Lütz, Erik Rodner, Joachim Denzler:
I Want To Know More: Efficient Multi-Class Incremental Learning Using Gaussian Processes.
Pattern Recognition and Image Analysis. Advances in Mathematical Theory and Applications (PRIA). 23 (3) : pp. 402-407. 2013.
[bibtex] [pdf]
2012
Alexander Freytag, Erik Rodner, Paul Bodesheim, Joachim Denzler:
Rapid Uncertainty Computation with Gaussian Processes and Histogram Intersection Kernels.
Asian Conference on Computer Vision (ACCV). Pages 511-524. 2012. Best Paper Honorable Mention Award
[bibtex] [pdf] [web] [doi] [presentation] [abstract]
2011
Alexander Lütz, Erik Rodner, Joachim Denzler:
Efficient Multi-Class Incremental Learning Using Gaussian Processes.
Open German-Russian Workshop on Pattern Recognition and Image Understanding (OGRW). Pages 182-185. 2011.
[bibtex] [pdf] [abstract]