8 citations · 8 across the 2 of their papers we have counts for
4 papers
A Review of Uncertainty Calibration in Pretrained Object Detectors
Denis Huseljic, Marek Herde, Mehmet Muejde +1
In the field of deep learning based computer vision, the development of deep object detection has led to unique paradigms (e.g., two-stage or set-based) and architectures (e.g., Fa…
Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders
Felix Möller, Diego Botache, Denis Huseljic +3
Deep neural networks often suffer from overconfidence which can be partly remedied by improved out-of-distribution detection. For this purpose, we propose a novel approach that all…
Toward Optimal Probabilistic Active Learning Using a Bayesian Approach
Daniel Kottke, Marek Herde, Christoph Sandrock +3
Gathering labeled data to train well-performing machine learning models is one of the critical challenges in many applications. Active learning aims at reducing the labeling costs…
Limitations of Assessing Active Learning Performance at Runtime
Daniel Kottke, Jim Schellinger, Denis Huseljic +1
Classification algorithms aim to predict an unknown label (e.g., a quality class) for a new instance (e.g., a product). Therefore, training samples (instances and labels) are used…