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05/07/2023
Learned Wyner-Ziv Compressors Recover Binning
We consider lossy compression of an information source when the decoder ...
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05/17/2022
Do Neural Networks Compress Manifolds Optimally?
Artificial Neural-Network-based (ANN-based) lossy compressors have recen...
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01/07/2022
Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory
A significant bottleneck in federated learning is the network communicat...
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10/29/2021
Optimal Compression of Locally Differentially Private Mechanisms
Compressing the output of ϵ-locally differentially private (LDP) randomi...
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07/26/2021
Towards Generative Video Compression
We present a neural video compression method based on generative adversa...
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06/08/2021
On the relation between statistical learning and perceptual distances
It has been demonstrated many times that the behavior of the human visua...
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04/26/2021
3D Scene Compression through Entropy Penalized Neural Representation Functions
Some forms of novel visual media enable the viewer to explore a 3D scene...
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11/10/2020
Neural Networks Optimally Compress the Sawbridge
Neural-network-based compressors have proven to be remarkably effective ...
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07/23/2020
End-to-end Learning of Compressible Features
Pre-trained convolutional neural networks (CNNs) are powerful off-the-sh...
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07/06/2020
Nonlinear Transform Coding
We review a class of methods that can be collected under the name nonlin...
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06/11/2020
An Unsupervised Information-Theoretic Perceptual Quality Metric
Tractable models of human perception have proved to be challenging to bu...
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12/18/2019
Computationally Efficient Neural Image Compression
Image compression using neural networks have reached or exceeded non-neu...
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06/15/2019
Model Compression by Entropy Penalized Reparameterization
We describe an end-to-end neural network weight compression approach tha...
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03/03/2019
Accelerating Training of Deep Neural Networks with a Standardization Loss
A significant advance in accelerating neural network training has been t...
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09/08/2018
Joint Autoregressive and Hierarchical Priors for Learned Image Compression
Recent models for learned image compression are based on autoencoders, l...
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08/01/2018
Towards a Semantic Perceptual Image Metric
We present a full reference, perceptual image metric based on VGG-16, an...
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02/01/2018
Variational image compression with a scale hyperprior
We describe an end-to-end trainable model for image compression based on...
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10/06/2017
Eigen-Distortions of Hierarchical Representations
We develop a method for comparing hierarchical image representations in ...
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01/23/2017
Perceptually Optimized Image Rendering
We develop a framework for rendering photographic images, taking into ac...
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11/05/2016
End-to-end Optimized Image Compression
We describe an image compression method, consisting of a nonlinear analy...
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07/06/2015
A model of sensory neural responses in the presence of unknown modulatory inputs
Neural responses are highly variable, and some portion of this variabili...
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12/20/2014