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Marc Aubreville

4 accepted papers

2024

Würstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models

ICLR 2024oral

We introduce Würstchen, a novel architecture for text-to-image synthesis that combines competitive performance with unprecedented cost-effectiveness for large-scale text-to-image diffusion models. A key contribution of our work is to develop a latent diffusion technique in which we learn a detailed…

Cited by 97SourcePDFScholar
2023

Why Is the Winner the Best?

CVPR 2023poster

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and…

Cited by 29SourcePDFScholar
2020

CLCNET: Deep Learning-Based Noise Reduction for Hearing aids using Complex Linear Coding

ICASSP 2020accepted

Noise reduction is an important part of modern hearing aids and is included in most commercially available devices. Deep learning-based state-of-the-art algorithms, however, either do not consider real-time and frequency resolution constrains or result in poor quality under very noisy conditions.To…

Cited by 0SourceScholar
2015

Directionality assessment of adaptive binaural beamforming with noise suppression in hearing aids

ICASSP 2015accepted

In this work we present a new method for assessment of directionality in modern hearing aids, which has the benefit of simulating both a target source and an interfering source at the same time using speech signals. We show the benefits and the limitations of the method and present measurements for…

Cited by 0SourceScholar