3 citations · 5 across the 2 of their papers we have counts for
2 papers
cs.CV2022★ 2 cited
Reducing the Amount of Real World Data for Object Detector Training with Synthetic Data
Sven Burdorf, Karoline Plum, Daniel Hasenklever
A number of studies have investigated the training of neural networks with synthetic data for applications in the real world. The aim of this study is to quantify how much real wor…
cs.LG2019★ 3 cited
Unsupervised training of a deep clustering model for multichannel blind source separation
Lukas Drude, Daniel Hasenklever, Reinhold Haeb-Umbach
We propose a training scheme to train neural network-based source separation algorithms from scratch when parallel clean data is unavailable. In particular, we demonstrate that an…