Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Strümpler, Yannick"'
Neural Fields (NFs) have gained momentum as a tool for compressing various data modalities - e.g. images and videos. This work leverages previous advances and proposes a novel NF-based compression algorithm for 3D data. We derive two versions of our
Externí odkaz:
http://arxiv.org/abs/2311.13009
Recently Implicit Neural Representations (INRs) gained attention as a novel and effective representation for various data types. Thus far, prior work mostly focused on optimizing their reconstruction performance. This work investigates INRs from a no
Externí odkaz:
http://arxiv.org/abs/2112.04267
Autor:
Postels, Janis, Blum, Hermann, Strümpler, Yannick, Cadena, Cesar, Siegwart, Roland, Van Gool, Luc, Tombari, Federico
The distribution of a neural network's latent representations has been successfully used to detect out-of-distribution (OOD) data. This work investigates whether this distribution moreover correlates with a model's epistemic uncertainty, thus indicat
Externí odkaz:
http://arxiv.org/abs/2012.03082
In recent years we have witnessed an increasing interest in applying Deep Neural Networks (DNNs) to improve the rate-distortion performance in image compression. However, the existing approaches either train a post-processing DNN on the decoder side,
Externí odkaz:
http://arxiv.org/abs/2009.12927