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Neural implicit shape representations are an emerging paradigm that offers many potential benefits over conventional discrete representations, including memory efficiency at a high spatial resolution.
Deep structured implicit functions
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Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Meta-sgd: Learning to learn quickly for few-shot learning
Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li · 2017
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Neural scene representation and rendering
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Conditional neural processes
Marta Garnelo, Dan Rosenbaum, Chris J Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo J Rezende, and SM Eslami · 2018
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Differentiable image parameterizations
Alexander Mordvintsev, Nicola Pezzotti, Ludwig Schubert, and Chris Olah · 2018
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Learning free-form deformations for 3d object reconstruction
Dominic Jack, Jhony K. Pontes, Sridha Sridharan, Clinton Fookes, Sareh Shirazi, Frédéric Maire, and Anders Eriksson · 2018
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Reptile: a scalable metalearning algorithm
Alex Nichol and John Schulman · 2018
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Attentive neural processes
Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami, Dan Rosenbaum, Oriol Vinyals, and Yee Whye Teh · 2019
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Meta-learning with latent embedding optimization
Andrei A Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, and Raia Hadsell · 2019
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Meta-learning with implicit gradients
Aravind Rajeswaran, Chelsea Finn, Sham M Kakade, and Sergey Levine · 2019
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Multi-digit mnist for few-shot learning, 2019
Shao-Hua Sun · 2019
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Metafun: Meta-learning with iterative functional updates
Jin Xu, Jean-Francois Ton, Hyunjik Kim, Adam R Kosiorek, and Yee Whye Teh · 2019
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Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
Alex Kendall, Yarin Gal, and Roberto Cipolla · 2018
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Deepsdf: Learning continuous signed distance functions for shape representation
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove · 2019
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On the limitations of representing functions on sets
Edward Wagstaff, Fabian B Fuchs, Martin Engelcke, Ingmar Posner, and Michael Osborne · 2019
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Scene representation networks: Continuous 3d-structure-aware neural scene representations
Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein · 2019
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Implicit surface representations as layers in neural networks
Mateusz Michalkiewicz, Jhony K Pontes, Dominic Jack, Mahsa Baktashmotlagh, and Anders Eriksson · 2019
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Sal: Sign agnostic learning of shapes from raw data
Matan Atzmon and Yaron Lipman · 2019
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Learning implicit fields for generative shape modeling
Zhiqin Chen and Hao Zhang · 2019
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Marc Finzi, Samuel Stanton, Pavel Izmailov, and Andrew Gordon Wilson · 2020
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Inferring semantic information with 3d neural scene representations
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Implicit geometric regularization for learning shapes
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Local implicit grid representations for 3d scenes
Chiyu Max Jiang, Avneesh Sud, Ameesh Makadia, Jingwei Huang, Matthias Nießner, and Thomas Funkhouser · 2020
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Convolutional occupancy networks
Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger · 2020
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Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision
Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger · 2020
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Nerf: Representing scenes as neural radiance fields for view synthesis
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State of the art on neural rendering
Ayush Tewari, Ohad Fried, Justus Thies, Vincent Sitzmann, Stephen Lombardi, Kalyan Sunkavalli, Ricardo Martin-Brualla, Tomas Simon, Jason Saragih, Matthias Nießner, et al · 2020
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Meta-learning in neural networks: A survey
Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey · 2020
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