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We propose a new simple approach for image compression: instead of storing the RGB values for each pixel of an image, we store the weights of a neural network overfitted to the image.
Kodak Dataset
Kodak · 1991
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Compositional pattern producing networks: A novel abstraction of development
Kenneth O Stanley · 2007
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Iterative Refinement of the Approximate Posterior for Directed Belief Networks
R. Devon Hjelm, Kyunghyun Cho, Junyoung Chung, Russ Salakhutdinov, Vince Calhoun, and Nebojsa Jojic · 2016
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Bayesian Compression for Deep Learning
Christos Louizos, Karen Ullrich, and Max Welling · 2017
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Soft Weight-Sharing for Neural Network Compression
Karen Ullrich, Edward Meeds, and Max Welling · 2017
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Variational image compression with a scale hyperprior
Johannes Ballé, David Minnen, Saurabh Singh, Sung Jin Hwang, and Nick Johnston · 2018
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Inference Suboptimality in Variational Autoencoders
Chris Cremer, Xuechen Li, and David Duvenaud · 2018
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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
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Semi-Amortized Variational Autoencoders
Yoon Kim, Sam Wiseman, Andrew C. Miller, David Sontag, and Alexander M. Rush · 2018
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Quantizing deep convolutional networks for efficient inference: A whitepaper
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On the challenges of learning with inference networks on sparse, high-dimensional data
Rahul G. Krishnan, Dawen Liang, and Matthew Hoffman · 2018
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Iterative Amortized Inference
Joseph Marino, Yisong Yue, and Stephan Mandt · 2018
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Joint Autoregressive and Hierarchical Priors for Learned Image Compression
David Minnen, Johannes Ballé, and George Toderici · 2018
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RenderNet: A deep convolutional network for differentiable rendering from 3D shapes
Thu Nguyen-Phuoc, Chuan Li, Stephen Balaban, and Yong-Liang Yang · 2018
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The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies
Ronen Basri, David Jacobs, Yoni Kasten, and Shira Kritchman · 2019
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Content Adaptive Optimization for Neural Image Compression
Joaquim Campos, Simon Meierhans, Abdelaziz Djelouah, and Christopher Schroers · 2019
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Learning Implicit Fields for Generative Shape Modeling
Zhiqin Chen and Hao Zhang · 2019
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Neural Architecture Search: A Survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2019
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Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters
Marton Havasi, Robert Peharz, and José Miguel Hernández-Lobato · 2019
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Context-adaptive Entropy Model for End-to-end Optimized Image Compression
Jooyoung Lee, Seunghyun Cho, and Seung-Kwon Beack · 2019
Variable Rate Image Compression with Content Adaptive Optimization
Tiansheng Guo, Jing Wang, Ze Cui, Yihui Feng, Yunying Ge, and Bo Bai · 2020
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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 · 2020
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MetaSDF: Meta-learning Signed Distance Functions
Vincent Sitzmann, Eric R. Chan, Richard Tucker, Noah Snavely, and Gordon Wetzstein · 2020
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Implicit Neural Representations with Periodic Activation Functions
Vincent Sitzmann, Julien N. P. Martel, Alexander W. Bergman, David B. Lindell, and Gordon Wetzstein · 2020
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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 · 2020
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Occupancy Flow: 4D Reconstruction by Learning Particle Dynamics
Michael Niemeyer, Lars Mescheder, Michael Oechsle, 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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PyTorch: An Imperative Style, High-Performance Deep Learning Library
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DeepVoxels: Learning Persistent 3D Feature Embeddings
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Compressai: a pytorch library and evaluation platform for end-to-end compression research
Jean Bégaint, Fabien Racapé, Simon Feltman, and Akshay Pushparaja · 2020
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Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules
Zhengxue Cheng, Heming Sun, Masaru Takeuchi, and Jiro Katto · 2020
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Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, and Ren Ng · 2020
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GRF: Learning a General Radiance Field for 3D Scene Representation and Rendering
Alex Trevithick and Bo Yang · 2020
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Bayesian Bits: Unifying Quantization and Pruning
Mart van Baalen, Christos Louizos, Markus Nagel, Rana Ali Amjad, Ying Wang, Tijmen Blankevoort, and Max Welling · 2020
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Improving Inference for Neural Image Compression
Yibo Yang, Robert Bamler, and Stephan Mandt · 2020
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pixelNeRF: Neural Radiance Fields from One or Few Images
Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa · 2020
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Per-title encode optimization
Anne Aaron, Zhi Li, Megha Manohara, Jan De Cock, and David Ronca · 2021
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Generative Models as Distributions of Functions
Emilien Dupont, Yee Whye Teh, and Arnaud Doucet · 2021
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Overfitting for Fun and Profit: Instance-Adaptive Data Compression
Ties van Rozendaal, Iris A M Huijben, and Taco S Cohen · 2021
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