136 citations · 213 across the 7 of their papers we have counts for
7 papers
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…
Part Detector Discovery in Deep Convolutional Neural Networks
Marcel Simon, Erik Rodner, Joachim Denzler
Current fine-grained classification approaches often rely on a robust localization of object parts to extract localized feature representations suitable for discrimination. However…
ARTOS -- Adaptive Real-Time Object Detection System
Björn Barz, Erik Rodner, Joachim Denzler
ARTOS is all about creating, tuning, and applying object detection models with just a few clicks. In particular, ARTOS facilitates learning of models for visual object detection by…