18 citations · 20 across the 5 of their papers we have counts for
8 papers
InDiReCT: Language-Guided Zero-Shot Deep Metric Learning for Images
Konstantin Kobs, Michael Steininger, Andreas Hotho
Common Deep Metric Learning (DML) datasets specify only one notion of similarity, e.g., two images in the Cars196 dataset are deemed similar if they show the same car model. We arg…
Semi-unsupervised Learning for Time Series Classification
Padraig Davidson, Michael Steininger, André Huhn +2
Time series are ubiquitous and therefore inherently hard to analyze and ultimately to label or cluster. With the rise of the Internet of Things (IoT) and its smart devices, data is…
Do Different Deep Metric Learning Losses Lead to Similar Learned Features?
Konstantin Kobs, Michael Steininger, Andrzej Dulny +1
Recent studies have shown that many deep metric learning loss functions perform very similarly under the same experimental conditions. One potential reason for this unexpected resu…
Anomaly Detection in Beehives: An Algorithm Comparison
Padraig Davidson, Michael Steininger, Florian Lautenschlager +2
Sensor-equipped beehives allow monitoring the living conditions of bees. Machine learning models can use the data of such hives to learn behavioral patterns and find anomalous even…
Deep Learning for Climate Model Output Statistics
Michael Steininger, Daniel Abel, Katrin Ziegler +3
Climate models are an important tool for the assessment of prospective climate change effects but they suffer from systematic and representation errors, especially for precipitatio…
Anomaly Detection in Beehives using Deep Recurrent Autoencoders
Padraig Davidson, Michael Steininger, Florian Lautenschlager +3
Precision beekeeping allows to monitor bees' living conditions by equipping beehives with sensors. The data recorded by these hives can be analyzed by machine learning models to le…
SimLoss: Class Similarities in Cross Entropy
Konstantin Kobs, Michael Steininger, Albin Zehe +2
One common loss function in neural network classification tasks is Categorical Cross Entropy (CCE), which punishes all misclassifications equally. However, classes often have an in…
MapLUR: Exploring a new Paradigm for Estimating Air Pollution using Deep Learning on Map Images
Michael Steininger, Konstantin Kobs, Albin Zehe +3
Land-use regression (LUR) models are important for the assessment of air pollution concentrations in areas without measurement stations. While many such models exist, they often us…