no code implementations • 14 Sep 2023 • Syed Sha Qutub, Neslihan Kose, Rafael Rosales, Michael Paulitsch, Korbinian Hagn, Florian Geissler, Yang Peng, Gereon Hinz, Alois Knoll
The proposed loss functions in BEA improve the confidence score calibration and lower the uncertainty error, which results in a better distinction of true and false positives and, eventually, higher accuracy of the object detection models.
no code implementations • 15 Mar 2023 • Rafael Rosales, Pablo Munoz, Michael Paulitsch
We compare ensembles created with diverse model architectures trained either independently or through a Neural Architecture Search technique and evaluate the correlation of prediction-based and attribution-based diversity to the final ensemble accuracy.
no code implementations • 3 Mar 2023 • Rafael Rosales, Peter Popov, Michael Paulitsch
The key idea is to create successive model experts for samples that were difficult (not necessarily incorrectly classified) by the preceding model.
no code implementations • 3 Mar 2022 • Christian Herglotz, Rafael Rosales, Michael Glass, Jürgen Teich, André Kaup
Finding the best possible encoding decisions for compressing a video sequence is a highly complex problem.