333 citations · 1.1k across the 20 of their papers we have counts for
6 papers · 1 filter
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…
ImageNet pre-trained models with batch normalization
Marcel Simon, Erik Rodner, Joachim Denzler
Convolutional neural networks (CNN) pre-trained on ImageNet are the backbone of most state-of-the-art approaches. In this paper, we present a new set of pre-trained models with pop…
Maximally Divergent Intervals for Anomaly Detection
Erik Rodner, Björn Barz, Yanira Guanche +5
We present new methods for batch anomaly detection in multivariate time series. Our methods are based on maximizing the Kullback-Leibler divergence between the data distribution wi…
Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches
Erik Rodner, Marcel Simon, Robert B. Fisher +1
In this paper, we study the sensitivity of CNN outputs with respect to image transformations and noise in the area of fine-grained recognition. In particular, we answer the followi…
Impatient DNNs - Deep Neural Networks with Dynamic Time Budgets
Manuel Amthor, Erik Rodner, Joachim Denzler
We propose Impatient Deep Neural Networks (DNNs) which deal with dynamic time budgets during application. They allow for individual budgets given a priori for each test example and…
Neither Quick Nor Proper -- Evaluation of QuickProp for Learning Deep Neural Networks
Clemens-Alexander Brust, Sven Sickert, Marcel Simon +2
Neural networks and especially convolutional neural networks are of great interest in current computer vision research. However, many techniques, extensions, and modifications have…