Short bio

I am working as a research scientist in the corporate research department of Carl Zeiss AG.

Previously, I was a senior researcher in the computer vision group at the Friedrich Schiller University of Jena (Germany). I finished my PhD in 2011 supervised by Joachim Denzler (PhD topic: transfer learning, learning with few examples) and I was heading the machine learning and visual recognition part of the group.

In 2012/13, I worked as a PostDoc in the computer vision group of Trevor Darrell at ICSI/EECS (UC Berkeley, California), where I focused on domain adaptation and open-vocabulary recognition. In general, my research interests are mainly machine learning aspects of computer vision tasks, such as lifelong learning for object recognition and scene understanding as well as general data analytics in the life sciences. Furthermore, there is a wide range of applications I have worked on, including bird species recognition, bio-medical image analysis, and driver assistance systems.

Recent publications

2017

Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning.
Marc Aubreville and Christian Knipfer and Nicolai Oetter and Christian Jaremenko and Erik Rodner and Joachim Denzler and Christopher Bohr and Helmut Neumannt and Florian Stelzle and Andreas Maier.
arXiv preprint arXiv:1703.01622. 2017. BibTeX www
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). 253-280. 2017. BibTeX pdf www
Fast Learning and Prediction for Object Detection using Whitened CNN Features.
Björn Barz and Erik Rodner and Christoph Käding and Joachim Denzler.
arXiv preprint arXiv:1704.02930. 2017. BibTeX www
Generalized orderless pooling performs implicit salient matching.
Marcel Simon and Erik Rodner and Yang Gao and Trevor Darrell and Joachim Denzler.
International Conference on Computer Vision (ICCV). 2017. (accepted for publication) BibTeX

2016

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 www supplementary
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 www
Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches.
Erik Rodner and Marcel Simon and Bob Fisher and Joachim Denzler.
British Machine Vision Conference (BMVC). 2016. BibTeX pdf supplementary
Impatient DNNs - Deep Neural Networks with Dynamic Time Budgets.
Manuel Amthor and Erik Rodner and Joachim Denzler.
British Machine Vision Conference (BMVC). 2016. BibTeX pdf
Chimpanzee Faces in the Wild: Log-Euclidean CNNs for Predicting Identities and Attributes of Primates.
Alexander Freytag and Erik Rodner and Marcel Simon and Alexander Loos and Hjalmar Kühl and Joachim Denzler.
German Conference on Pattern Recognition (GCPR). 51-63. 2016. BibTeX pdf www supplementary more ...

Abstract: In this paper, we investigate how to predict attributes of chimpanzees such as identity, age, age group, and gender. We build on convolutional neural networks, which lead to significantly superior results compared with previous state-of-the-art on hand-crafted recognition pipelines. In addition, we show how to further increase discrimination abilities of CNN activations by the Log-Euclidean framework on top of bilinear pooling. We finally introduce two curated datasets consisting of chimpanzee faces with detailed meta-information to stimulate further research. Our results can serve as the foundation for automated large-scale animal monitoring and analysis.
Neither Quick Nor Proper -- Evaluation of QuickProp for Learning Deep Neural Networks.
Clemens-Alexander Brust and Sven Sickert and Marcel Simon and Erik Rodner and Joachim Denzler.
arXiv preprint arXiv:1606.04333. 2016. BibTeX pdf www more ...

Abstract: Neural networks and especially convolutional neural networks are of great interest in current computer vision research. However, many techniques, extensions, and modifications have been published in the past, which are not yet used by current approaches. In this paper, we study the application of a method called QuickProp for training of deep neural networks. In particular, we apply QuickProp during learning and testing of fully convolutional networks for the task of semantic segmentation. We compare QuickProp empirically with gradient descent, which is the current standard method. Experiments suggest that QuickProp can not compete with standard gradient descent techniques for complex computer vision tasks like semantic segmentation.
Maximally Divergent Intervals for Anomaly Detection.
Erik Rodner and Björn Barz and Yanira Guanche and Milan Flach and Miguel Mahecha and Paul Bodesheim and Markus Reichstein and Joachim Denzler.
ICML Workshop on Anomaly Detection (ICML-WS). 2016. Best Paper Award BibTeX pdf code
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). 179-191. 2016. BibTeX pdf www code supplementary more ...

Abstract: Incremental learning of visual concepts is one step towards reaching human capabilities beyond closed-world assumptions. Besides recent progress, it remains one of the fundamental challenges in computer vision and machine learning. Along that path, techniques are needed which allow for actively selecting informative examples from a huge pool of unlabeled images to be annotated by application experts. Whereas a manifold of active learning techniques exists, they commonly suffer from one of two drawbacks: (i) either they do not work reliably on challenging real-world data or (ii) they are kernel-based and not scalable with the magnitudes of data current vision applications need to deal with. Therefore, we present an active learning and discovery approach which can deal with huge collections of unlabeled real-world data. Our approach is based on the expected model output change principle and overcomes previous scalability issues. We present experiments on the large-scale MS-COCO dataset and on a dataset provided by biodiversity researchers. Obtained results reveal that our technique clearly improves accuracy after just a few annotations. At the same time, it outperforms previous active learning approaches in academic and real-world scenarios.
Convolutional Neural Networks as a Computational Model for the Underlying Processes of Aesthetics Perception.
Joachim Denzler and Erik Rodner and Marcel Simon.
ECCV Workshop on Computer Vision for Art Analysis. 2016. BibTeX pdf www

Teaching

I am involved in university teaching since 2008. Besides several projects, exercises, and seminars in the computer vision area, I also give two lectures in Jena.

Software and datasets

GitHub

  • NICE library

    C++ library for computer vision and machine learning.
  • CN24: CNN semantic segmentation

    Convolutional neural network library for semantic segmentation and pixel-wise labeling. GPU acceleration is based on OpenCL. Developed by Clemens-Alexander Brust during an undergraduate thesis
  • ARTOS object detection

    Object detection library and GUI in C++ and python, which allows for learning HOG detection models from ImageNet in a few seconds. Developed by Björn Barz during an undergraduate thesis supervised by myself.

Posts

  • Office 2.0 dataset

    To perform quantitative experiments with object detection and domain adaptation, I used Amazon Mechanical Turk to annotate the Office dataset of Kate Saenko. 02/01/14
  • Moth datasets

    These datasets can be used for fine-grained recognition with a large number of categories. The images depict several moth species from Ecuador and Costa Rica and have been prepared and evaluated together with Wolfgang Wägele (ZFMK) and Gunnar Brehm (University of Jena). See our paper at the CVPR 2015 workshop for details. 04/12/15