no code implementations • 20 Jan 2020 • Clemens-Alexander Brust, Christoph Käding, Joachim Denzler
By selecting unlabeled examples that are promising in terms of model improvement and only asking for respective labels, active learning can increase the efficiency of the labeling process in terms of time and cost.
no code implementations • 26 Sep 2018 • Clemens-Alexander Brust, Christoph Käding, Joachim Denzler
In this paper, we combine a novel method of active learning for object detection with an incremental learning scheme to enable continuous exploration of new unlabeled datasets.
1 code implementation • 7 Sep 2018 • Björn Barz, Christoph Käding, Joachim Denzler
We propose Information-Theoretic Active Learning (ITAL), a novel batch-mode active learning method for binary classification, and apply it for acquiring meaningful user feedback in the context of content-based image retrieval.
no code implementations • 10 Apr 2017 • Björn Barz, Erik Rodner, Christoph Käding, Joachim Denzler
We combine features extracted from pre-trained convolutional neural networks (CNNs) with the fast, linear Exemplar-LDA classifier to get the advantages of both: the high detection performance of CNNs, automatic feature engineering, fast model learning from few training samples and efficient sliding-window detection.
no code implementations • 19 Dec 2016 • Christoph Käding, Erik Rodner, Alexander Freytag, Joachim Denzler
The demands on visual recognition systems do not end with the complexity offered by current large-scale image datasets, such as ImageNet.