about this site

Welcome to my blog, where I periodically post about data science and machine learning, maker-projects and other tech related topics.

It is a mixed blog so if you are interested in a single topic you can subscribe the respective topic's feed:

<> all topics <> data science <> maker space <> photography <> off topic


September, 2019

Active Learning for Image Recognition using a Visualization-Based User Interface

Christian Limberg, Kathrin Krieger, Heiko Wersing, Helge Ritter

International Conference on Artificial Neural Networks (ICANN)

Abstract: This paper introduces a novel approach for querying samples to be labeled in active learning for image recognition. The user is able to efficiently label images with a visualization for training a classifier. This visualization is achieved by using dimension reduction techniques to create a 2D feature embedding from high-dimensional features. This is made possible by a querying strategy specifically designed for the visualization, seeking optimized bounding-box views for subsequent labeling. The approach is implemented in a web-based prototype. It is compared in-depth to other active learning querying strategies within a user study we conducted with 31 participants on a challenging data set. While using our approach, the participants could train a more accurate classifier than with the other approaches. Additionally, we demonstrate that due to the visualization, the number of labeled samples increases and also the label quality improves.

October, 2018

Improving Active Learning by Avoiding Ambiguous Samples

Christian Limberg, Heiko Wersing, Helge Ritter

International Conference on Artificial Neural Networks (ICANN)

Abstract: If label information in a classification task is expensive, it can be beneficial to use active learning to get the most informative samples to label by a human. However, there can be samples which are meaningless to the human or recorded wrongly. If these samples are near the classifier’s decision boundary, they are queried repeatedly for labeling. This is inefficient for training because the human can not label these samples correctly and this may lower human acceptance. We introduce an approach to compensate the problem of ambiguous samples by excluding clustered samples from labeling. We compare this approach to other state-of-the-art methods. We further show that we can improve the accuracy in active learning and reduce the number of ambiguous samples queried while training.

April, 2018

Efficient Accuracy Estimation for Instance-Based Incremental Active Learning

Christian Limberg, Heiko Wersing, Helge Ritter

European Symposium on Artificial Neural Networks (ESANN)

Abstract: Estimating systems accuracy is crucial for applications of incremental learning. In this paper, we introduce the Distogram Estimation (DGE) approach to estimate the accuracy of instance-based classifiers. By calculating relative distances to samples it is possible to train an offline regression model, capable of predicting the classifiers accuracy on unseen data. Our approach requires only a few supervised samples for training and can instantaneously be applied on unseen data afterwards. We evaluate our method on five benchmark data sets and for a robot object recognition task. Our algorithm clearly outperforms two baseline methods both for random and active selection of incremental training examples.


ALeFra – Active Learning Framework

Convert any classifier (online and offline) in an active trainable classifier. The classifier can be trained easily and the training progress can be visualized in different ways to better understand what is going on. Please check the Github page for full example usages and interactive result plots.


You can contact me via my email address climberg[at]techfak[d0t]uni-bielefeld[d0t]de

recent posts

2019-03-10 in maker space
3D prints for a loving home
2019-02-27 in off topic
Determining the total revenue of a blackmailer: Bitcoin is offering new possiblities
2019-02-21 in photography
Skyline Frankfurt
2019-02-19 in photography
Old train station Löhne
2019-02-15 in maker space
Tiny Core - a very small linux distribution for the Raspberry PI (piCore)

about me

I am a PhD-student from Frankfurt in Germany. My research topics are machine learning and human robot interaction. More precisely I work on active learning and cooperative intelligence in task completion right now.

In my free time I like to make things. Some of them are tech related, but others are not. I am also interested in programming, web-programming, robotics and drones, IoT, photography and making delicious filter coffee or espresso. Last but not least I am a big fan and collector of retro video games, especially N64 and SNES. You can take a look at my complete collection.

just me