Fetching the paper…
Reading the bibliography…
Emerging Implicit Neural Representation (INR) is a promising data compression technique, which represents the data using the parameters of a Deep Neural Network (DNN).
A method for the construction of minimum-redundancy codes
David A Huffman · 1952
Earlier work this paper cites.
Adaptive mixtures of local experts
Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton · 1991
Earlier work this paper cites.
JPEG: Still image data compression standard
William B Pennebaker and Joan L Mitchell · 1992
Earlier work this paper cites.
The jpeg still picture compression standard
Gregory K Wallace · 1992
Earlier work this paper cites.
Mixtures of gaussian processes
Volker Tresp · 2000
Earlier work this paper cites.
A parallel mixture of svms for very large scale problems
Ronan Collobert, Samy Bengio, and Yoshua Bengio · 2001
Earlier work this paper cites.
An overview of the jpeg 2000 still image compression standard
Majid Rabbani and Rajan Joshi · 2002
Earlier work this paper cites.
Overview of the h. 264/avc video coding standard
Thomas Wiegand, Gary J Sullivan, Gisle Bjontegaard, and Ajay Luthra · 2003
Earlier work this paper cites.
Finite mixture and Markov switching models
Sylvia Frühwirth-Schnatter and Sylvia Frèuhwirth-Schnatter · 2006
Earlier work this paper cites.
Overview of the high efficiency video coding (hevc) standard
Gary J Sullivan, Jens-Rainer Ohm, Woo-Jin Han, and Thomas Wiegand · 2012
Earlier work this paper cites.
Learning factored representations in a deep mixture of experts
David Eigen, Marc’Aurelio Ranzato, and Ilya Sutskever · 2013
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Earlier work this paper cites.
Pruning convolutional neural networks for resource efficient inference
Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz · 2016
Earlier work this paper cites.
Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
Earlier work this paper cites.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
Cited alongside, same era.
Quantization and training of neural networks for efficient integer-arithmetic-only inference
Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko · 2018
Cited alongside, same era.
Dvc: An end-to-end deep video compression framework
Guo Lu, Wanli Ouyang, Dong Xu, Xiaoyun Zhang, Chunlei Cai, and Zhiyong Gao · 2019
Cited alongside, same era.
Scale-space flow for end-to-end optimized video compression
Eirikur Agustsson, David Minnen, Nick Johnston, Johannes Balle, Sung Jin Hwang, and George Toderici · 2020
Cited alongside, same era.
Gshard: Scaling giant models with conditional computation and automatic sharding
Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, and Zhifeng Chen · 2020
Implicit neural video compression
Yunfan Zhang, Ties van Rozendaal, Johann Brehmer, Markus Nagel, and Taco Cohen · 2021
Later among the works it cites.
Knowledge distillation: A good teacher is patient and consistent
Lucas Beyer, Xiaohua Zhai, Amélie Royer, Larisa Markeeva, Rohan Anil, and Alexander Kolesnikov · 2022
Later among the works it cites.
Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
William Fedus, Barret Zoph, and Noam Shazeer · 2022
Later among the works it cites.
Plenoxels: Radiance fields without neural networks
Sara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa · 2022
Later among the works it cites.
A survey of quantization methods for efficient neural network inference
Amir Gholami, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael W Mahoney, and Kurt Keutzer · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Implicit neural representations with periodic activation functions
Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein · 2020
Cited alongside, same era.
Nerv: Neural representations for videos
Hao Chen, Bo He, Hanyu Wang, Yixuan Ren, Ser Nam Lim, and Abhinav Shrivastava · 2021
Cited alongside, same era.
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
Cited alongside, same era.
A white paper on neural network quantization
Markus Nagel, Marios Fournarakis, Rana Ali Amjad, Yelysei Bondarenko, Mart Van Baalen, and Tijmen Blankevoort · 2021
Cited alongside, same era.
Hypernerf: A higher-dimensional representation for topologically varying neural radiance fields
Keunhong Park, Utkarsh Sinha, Peter Hedman, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Ricardo Martin-Brualla, and Steven M Seitz · 2021
Cited alongside, same era.
D-nerf: Neural radiance fields for dynamic scenes
Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer · 2021
Cited alongside, same era.
Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction
Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang · 2021
Cited alongside, same era.
Changho Hwang, Wei Cui, Yifan Xiong, Ziyue Yang, Ze Liu, Han Hu, Zilong Wang, Rafael Salas, Jithin Jose, Prabhat Ram, et al · 2022
Later among the works it cites.
Instant neural graphics primitives with a multiresolution hash encoding
Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller · 2022
Later among the works it cites.
Nerf-slam: Real-time dense monocular slam with neural radiance fields
Antoni Rosinol, John J Leonard, and Luca Carlone · 2022
Later among the works it cites.
Implicit neural representations for image compression
Yannick Strümpler, Janis Postels, Ren Yang, Luc Van Gool, and Federico Tombari · 2022
Later among the works it cites.
Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction
Cheng Sun, Min Sun, and Hwann-Tzong Chen · 2022
Later among the works it cites.
Voxurf: Voxel-based efficient and accurate neural surface reconstruction
Tong Wu, Jiaqi Wang, Xingang Pan, Xudong Xu, Christian Theobalt, Ziwei Liu, and Dahua Lin · 2022
Later among the works it cites.
Nice-slam: Neural implicit scalable encoding for slam
Zihan Zhu, Songyou Peng, Viktor Larsson, Weiwei Xu, Hujun Bao, Zhaopeng Cui, Martin R Oswald, and Marc Pollefeys · 2022
Later among the works it cites.
Switch-nerf: Learning scene decomposition with mixture of experts for large-scale neural radiance fields
Zhenxing Mi and Dan Xu · 2023
Closest in time.