June 7, 2024, 4:44 a.m. | Sebastian Loeschcke, Dan Wang, Christian Leth-Espensen, Serge Belongie, Michael J. Kastoryano, Sagie Benaim

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.04332v1 Announce Type: cross
Abstract: The ability to learn compact, high-quality, and easy-to-optimize representations for visual data is paramount to many applications such as novel view synthesis and 3D reconstruction. Recent work has shown substantial success in using tensor networks to design such compact and high-quality representations. However, the ability to optimize tensor-based representations, and in particular, the highly compact tensor train representation, is still lacking. This has prevented practitioners from deploying the full potential of tensor networks for visual …

arxiv compact cs.cv cs.lg tensor trains type visual

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