Incremental Learning

Team

Daphne Auer, Julia Böhlke, 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
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]
2016
Lifelong Learning for Visual Recognition Systems
Alexander Freytag. 2016. ISBN 9783843929950
[bibtex] [pdf] [web]
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]
2013
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]
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]