How to Train Neural Field Representations: A Comprehensive Study and
Benchmark

How to Train Neural Field Representations: A Comprehensive Study and Benchmark

16 December 2023

Riccardo Valperga

David M. Knigge

Miltiadis Kofinas

Papers citing "How to Train Neural Field Representations: A Comprehensive Study and Benchmark"

10 / 10 papers shown

Title
End-to-End Implicit Neural Representations for Classification Alexander Gielisse J. C. V. Gemert 42 0 0 23 Mar 2025
ARC: Anchored Representation Clouds for High-Resolution INR Classification Joost Luijmes Alexander Gielisse Roman Knyazhitskiy J. C. V. Gemert 47 1 0 19 Mar 2025
On the Internal Representations of Graph Metanetworks Taesun Yeom Jaeho Lee GNN 54 0 0 12 Mar 2025
Geometric Neural Process Fields Wenzhe Yin Zehao Xiao Jiayi Shen Yunlu Chen Cees G. M. Snoek J. Sonke E. Gavves AI4CE 38 0 0 04 Feb 2025
Fast Training of Sinusoidal Neural Fields via Scaling Initialization Taesun Yeom Sangyoon Lee Jaeho Lee 48 2 0 07 Oct 2024
Latent-INR: A Flexible Framework for Implicit Representations of Videos with Discriminative Semantics Shishira R. Maiya Anubhav Gupta M. Gwilliam Max Ehrlich Abhinav Shrivastava 33 3 1 05 Aug 2024
Grounding Continuous Representations in Geometry: Equivariant Neural Fields David R. Wessels David M. Knigge Samuele Papa Riccardo Valperga Sharvaree P. Vadgama E. Gavves Erik J. Bekkers 33 7 0 09 Jun 2024
Git Re-Basin: Merging Models modulo Permutation Symmetries Samuel K. Ainsworth J. Hayase S. Srinivasa MoMe 239 313 0 11 Sep 2022
From data to functa: Your data point is a function and you can treat it like one Emilien Dupont Hyunjik Kim S. M. Ali Eslami Danilo Jimenez Rezende Dan Rosenbaum TDI 3DPC 160 136 0 28 Jan 2022
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes David W. Romero Robert-Jan Bruintjes Jakub M. Tomczak Erik J. Bekkers Mark Hoogendoorn J. C. V. Gemert 74 81 0 15 Oct 2021