38 citations · 65 across the 5 of their papers we have counts for
5 papers · 1 filter
Active and Incremental Learning with Weak Supervision
Clemens-Alexander Brust, Christoph Käding, Joachim Denzler
Large amounts of labeled training data are one of the main contributors to the great success that deep models have achieved in the past. Label acquisition for tasks other than benc…
Active Learning for Deep Object Detection
Clemens-Alexander Brust, Christoph Käding, Joachim Denzler
The great success that deep models have achieved in the past is mainly owed to large amounts of labeled training data. However, the acquisition of labeled data for new tasks aside…
Information-Theoretic Active Learning for Content-Based Image Retrieval
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…
Fast Learning and Prediction for Object Detection using Whitened CNN Features
Björn Barz, Erik Rodner, Christoph Käding +1
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…
Active and Continuous Exploration with Deep Neural Networks and Expected Model Output Changes
Christoph Käding, Erik Rodner, Alexander Freytag +1
The demands on visual recognition systems do not end with the complexity offered by current large-scale image datasets, such as ImageNet. In consequence, we need curious and contin…