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Reducing the Amount of Real World Data for Object Detector Training with Synthetic Data
The training of object detection networks is especially enhanced by enriching the mixed dataset with classes underrepresented in the real world dataset.
Unsupervised training of a deep clustering model for multichannel blind source separation
We propose a training scheme to train neural network-based source separation algorithms from scratch when parallel clean data is unavailable.
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