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Generalizable implicit neural representation (INR) enables a single continuous function, i.e., a coordinate-based neural network, to represent multiple data instances by modulating its weights or intermediate features using latent codes.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Model-agnostic meta-learning for fast adaptation of deep networks
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Hypernetworks
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Relational inductive biases, deep learning, and graph networks
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Occupancy networks: Learning 3d reconstruction in function space
Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger · 2019
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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 spectral bias of neural networks
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, and Aaron Courville · 2019
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Generating diverse high-fidelity images with vq-vae-2
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Fast context adaptation via meta-learning
Luisa Zintgraf, Kyriacos Shiarli, Vitaly Kurin, Katja Hofmann, and Shimon Whiteson · 2019
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Frequency bias in neural networks for input of non-uniform density
Ronen Basri, Meirav Galun, Amnon Geifman, David Jacobs, Yoni Kasten, and Shira Kritchman · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Local implicit grid representations for 3d scenes
Chiyu Jiang, Avneesh Sud, Ameesh Makadia, Jingwei Huang, Matthias Nießner, Thomas Funkhouser, et al · 2020
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Implicit neural representations with periodic activation functions
Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein · 2020
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Fourier features let networks learn high frequency functions in low dimensional domains
Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, and Ren Ng · 2020
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Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields
Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan · 2021
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Nerv: Neural representations for videos
Hao Chen, Bo He, Hanyu Wang, Yixuan Ren, Ser Nam Lim, and Abhinav Shrivastava · 2021
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Learning continuous image representation with local implicit image function
Yinbo Chen, Sifei Liu, and Xiaolong Wang · 2021
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Coin++: Data agnostic neural compression
Emilien Dupont, Hrushikesh Loya, Milad Alizadeh, Adam Goliński, Yee Whye Teh, and Arnaud Doucet · 2022
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Relu fields: The little non-linearity that could
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Autoregressive image generation using residual quantization
Doyup Lee, Chiheon Kim, Saehoon Kim, Minsu Cho, and Wook-Shin Han · 2022
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Instant neural graphics primitives with a multiresolution hash encoding
Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller · 2022
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COIN: COmpression with implicit neural representations
Emilien Dupont, Adam Golinski, Milad Alizadeh, Yee Whye Teh, and Arnaud Doucet · 2021
Cited alongside, same era.
Modulated periodic activations for generalizable local functional representations
Ishit Mehta, Michaël Gharbi, Connelly Barnes, Eli Shechtman, Ravi Ramamoorthi, and Manmohan Chandraker · 2021
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Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng · 2021
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Light field networks: Neural scene representations with single-evaluation rendering
Vincent Sitzmann, Semon Rezchikov, Bill Freeman, Josh Tenenbaum, and Fredo Durand · 2021
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
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Learned initializations for optimizing coordinate-based neural representations
Matthew Tancik, Ben Mildenhall, Terrance Wang, Divi Schmidt, Pratul P Srinivasan, Jonathan T Barron, and Ren Ng · 2021
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Transformers as meta-learners for implicit neural representations
Yinbo Chen and Xiaolong Wang · 2022
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William Peebles and Saining Xie · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Block-nerf: Scalable large scene neural view synthesis
Matthew Tancik, Vincent Casser, Xinchen Yan, Sabeek Pradhan, Ben Mildenhall, Pratul P Srinivasan, Jonathan T Barron, and Henrik Kretzschmar · 2022
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Locally hierarchical auto-regressive modeling for image generation
Tackgeun You, Saehoon Kim, Chiheon Kim, Doyup Lee, and Bohyung Han · 2022
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Spatial functa: Scaling functa to imagenet classification and generation
Matthias Bauer, Emilien Dupont, Andy Brock, Dan Rosenbaum, Jonathan Schwarz, and Hyunjik Kim · 2023
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GeNVS: Generative novel view synthesis with 3D-aware diffusion models
Eric R. Chan, Koki Nagano, Matthew A. Chan, Alexander W. Bergman, Jeong Joon Park, Axel Levy, Miika Aittala, Shalini De Mello, Tero Karras, and Gordon Wetzstein · 2023
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Generalizable implicit neural representations via instance pattern composers
Chiheon Kim, Doyup Lee, Saehoon Kim, Minsu Cho, and Wook-Shin Han · 2023
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Novel view synthesis with diffusion models
Daniel Watson, William Chan, Ricardo Martin Brualla, Jonathan Ho, Andrea Tagliasacchi, and Mohammad Norouzi · 2023
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