Timezone: »
Variational Autoencoders (VAEs) have seen widespread use in learned image compression. They are used to learn expressive latent representations on which downstream compression methods can operate with high efficiency. Recently proposed 'bits-back' methods can indirectly encode the latent representation of images with codelength close to the relative entropy between the latent posterior and the prior. However, due to the underlying algorithm, these methods can only be used for lossless compression, and they only achieve their nominal efficiency when compressing multiple images simultaneously; they are inefficient for compressing single images. As an alternative, we propose a novel method, Relative Entropy Coding (REC), that can directly encode the latent representation with codelength close to the relative entropy for single images, supported by our empirical results obtained on the Cifar10, ImageNet32 and Kodak datasets. Moreover, unlike previous bits-back methods, REC is immediately applicable to lossy compression, where it is competitive with the state-of-the-art on the Kodak dataset.
Author Information
Gergely Flamich (University of Cambridge)
Marton Havasi (University of Cambridge)
José Miguel Hernández-Lobato (University of Cambridge)
More from the Same Authors
-
2021 : A Fresh Look at De Novo Molecular Design Benchmarks »
Austin Tripp · Gregor Simm · José Miguel Hernández-Lobato -
2021 : Depth Uncertainty Networks for Active Learning »
Chelsea Murray · James Allingham · Javier Antorán · José Miguel Hernández-Lobato -
2021 : Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning »
Zachary Nado · Neil Band · Mark Collier · Josip Djolonga · Mike Dusenberry · Sebastian Farquhar · Qixuan Feng · Angelos Filos · Marton Havasi · Rodolphe Jenatton · Ghassen Jerfel · Jeremiah Liu · Zelda Mariet · Jeremy Nixon · Shreyas Padhy · Jie Ren · Tim G. J. Rudner · Yeming Wen · Florian Wenzel · Kevin Murphy · D. Sculley · Balaji Lakshminarayanan · Jasper Snoek · Yarin Gal · Dustin Tran -
2022 : Flow Annealed Importance Sampling Bootstrap »
Laurence Midgley · Vincent Stimper · Gregor Simm · Bernhard Schölkopf · José Miguel Hernández-Lobato -
2022 : Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction »
Wenlin Chen · Austin Tripp · José Miguel Hernández-Lobato -
2022 : Learning Generative Models with Invariance to Symmetries »
James Allingham · Javier Antorán · Shreyas Padhy · Eric Nalisnick · José Miguel Hernández-Lobato -
2023 : Adam through a Second-Order Lens »
Ross Clarke · Baiyu Su · José Miguel Hernández-Lobato -
2023 : SE(3) Equivariant Augmented Coupling Flows »
Laurence Midgley · Vincent Stimper · Vincent Stimper · Javier Antorán · Emile Mathieu · Emile Mathieu · Bernhard Schölkopf · Bernhard Schölkopf · José Miguel Hernández-Lobato -
2023 : Retro-fallback: retrosynthetic planning in an uncertain world »
Austin Tripp · Krzysztof Maziarz · Sarah Lewis · Marwin Segler · José Miguel Hernández-Lobato -
2023 : Estimating optimal PAC-Bayes bounds with Hamiltonian Monte Carlo »
Szilvia Ujváry · Gergely Flamich · Vincent Fortuin · José Miguel Hernández-Lobato -
2023 Poster: Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent »
Jihao Andreas Lin · Javier Antorán · Shreyas Padhy · David Janz · José Miguel Hernández-Lobato · Alexander Terenin -
2023 Oral: Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent »
Jihao Andreas Lin · Javier Antorán · Shreyas Padhy · David Janz · José Miguel Hernández-Lobato · Alexander Terenin -
2023 Poster: Greedy Poisson Rejection Sampling »
Gergely Flamich -
2023 Poster: Tanimoto Random Features for Scalable Molecular Machine Learning »
Austin Tripp · Sergio Bacallado · Sukriti Singh · José Miguel Hernández-Lobato -
2023 Poster: Faster Relative Entropy Coding with Greedy Rejection Coding »
Gergely Flamich · Stratis Markou · José Miguel Hernández-Lobato -
2023 Poster: SE(3) Equivariant Augmented Coupling Flows »
Laurence Midgley · Vincent Stimper · Javier Antorán · Emile Mathieu · Bernhard Schölkopf · José Miguel Hernández-Lobato -
2023 Poster: Compression with Bayesian Implicit Neural Representations »
Zongyu Guo · Gergely Flamich · Jiajun He · Zhibo Chen · José Miguel Hernández-Lobato -
2022 : Panel »
Roman Garnett · José Miguel Hernández-Lobato · Eytan Bakshy · Syrine Belakaria · Stefanie Jegelka -
2022 Poster: Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo »
Ignacio Peis · Chao Ma · José Miguel Hernández-Lobato -
2021 Workshop: Deep Generative Models and Downstream Applications »
José Miguel Hernández-Lobato · Yingzhen Li · Yichuan Zhang · Cheng Zhang · Austin Tripp · Weiwei Pan · Oren Rippel -
2021 Poster: Functional Variational Inference based on Stochastic Process Generators »
Chao Ma · José Miguel Hernández-Lobato -
2021 Poster: Improving black-box optimization in VAE latent space using decoder uncertainty »
Pascal Notin · José Miguel Hernández-Lobato · Yarin Gal -
2020 Workshop: Machine Learning for Molecules »
José Miguel Hernández-Lobato · Matt Kusner · Brooks Paige · Marwin Segler · Jennifer Wei -
2020 : Jose Miguel Hernandez Lobato »
José Miguel Hernández-Lobato -
2020 Poster: Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining »
Austin Tripp · Erik Daxberger · José Miguel Hernández-Lobato -
2020 Poster: Depth Uncertainty in Neural Networks »
Javier Antorán · James Allingham · José Miguel Hernández-Lobato -
2020 Poster: VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data »
Chao Ma · Sebastian Tschiatschek · Richard Turner · José Miguel Hernández-Lobato · Cheng Zhang -
2020 Poster: Barking up the right tree: an approach to search over molecule synthesis DAGs »
John Bradshaw · Brooks Paige · Matt Kusner · Marwin Segler · José Miguel Hernández-Lobato -
2020 Spotlight: Barking up the right tree: an approach to search over molecule synthesis DAGs »
John Bradshaw · Brooks Paige · Matt Kusner · Marwin Segler · José Miguel Hernández-Lobato -
2020 Session: Orals & Spotlights Track 15: COVID/Applications/Composition »
José Miguel Hernández-Lobato · Oliver Stegle -
2019 : Poster Session »
Gergely Flamich · Shashanka Ubaru · Charles Zheng · Josip Djolonga · Kristoffer Wickstrøm · Diego Granziol · Konstantinos Pitas · Jun Li · Robert Williamson · Sangwoong Yoon · Kwot Sin Lee · Julian Zilly · Linda Petrini · Ian Fischer · Zhe Dong · Alexander Alemi · Bao-Ngoc Nguyen · Rob Brekelmans · Tailin Wu · Aditya Mahajan · Alexander Li · Kirankumar Shiragur · Yair Carmon · Linara Adilova · SHIYU LIU · Bang An · Sanjeeb Dash · Oktay Gunluk · Arya Mazumdar · Mehul Motani · Julia Rosenzweig · Michael Kamp · Marton Havasi · Leighton P Barnes · Zhengqing Zhou · Yi Hao · Dylan Foster · Yuval Benjamini · Nati Srebro · Michael Tschannen · Paul Rubenstein · Sylvain Gelly · John Duchi · Aaron Sidford · Robin Ru · Stefan Zohren · Murtaza Dalal · Michael A Osborne · Stephen J Roberts · Moses Charikar · Jayakumar Subramanian · Xiaodi Fan · Max Schwarzer · Nicholas Roberts · Simon Lacoste-Julien · Vinay Prabhu · Aram Galstyan · Greg Ver Steeg · Lalitha Sankar · Yung-Kyun Noh · Gautam Dasarathy · Frank Park · Ngai-Man (Man) Cheung · Ngoc-Trung Tran · Linxiao Yang · Ben Poole · Andrea Censi · Tristan Sylvain · R Devon Hjelm · Bangjie Liu · Jose Gallego-Posada · Tyler Sypherd · Kai Yang · Jan Nikolas Morshuis -
2019 : Poster session »
Sebastian Farquhar · Erik Daxberger · Andreas Look · Matt Benatan · Ruiyi Zhang · Marton Havasi · Fredrik Gustafsson · James A Brofos · Nabeel Seedat · Micha Livne · Ivan Ustyuzhaninov · Adam Cobb · Felix D McGregor · Patrick McClure · Tim R. Davidson · Gaurush Hiranandani · Sanjeev Arora · Masha Itkina · Didrik Nielsen · William Harvey · Matias Valdenegro-Toro · Stefano Peluchetti · Riccardo Moriconi · Tianyu Cui · Vaclav Smidl · Taylan Cemgil · Jack Fitzsimons · He Zhao · · mariana vargas vieyra · Apratim Bhattacharyya · Rahul Sharma · Geoffroy Dubourg-Felonneau · Jonathan Warrell · Slava Voloshynovskiy · Mihaela Rosca · Jiaming Song · Andrew Ross · Homa Fashandi · Ruiqi Gao · Hooshmand Shokri Razaghi · Joshua Chang · Tim Xiao · Vanessa Boehm · Giorgio Giannone · Ranganath Krishnan · Joe Davison · Arsenii Ashukha · Jeremiah Liu · Sicong (Sheldon) Huang · Evgenii Nikishin · Sunho Park · Nilesh Ahuja · Mahesh Subedar · · Artyom Gadetsky · Jhosimar Arias Figueroa · Tim G. J. Rudner · Waseem Aslam · Adrián Csiszárik · John Moberg · Ali Hebbal · Kathrin Grosse · Pekka Marttinen · Bang An · Hlynur Jónsson · Samuel Kessler · Abhishek Kumar · Mikhail Figurnov · Omesh Tickoo · Steindor Saemundsson · Ari Heljakka · Dániel Varga · Niklas Heim · Simone Rossi · Max Laves · Waseem Gharbieh · Nicholas Roberts · Luis Armando Pérez Rey · Matthew Willetts · Prithvijit Chakrabarty · Sumedh Ghaisas · Carl Shneider · Wray Buntine · Kamil Adamczewski · Xavier Gitiaux · Suwen Lin · Hao Fu · Gunnar Rätsch · Aidan Gomez · Erik Bodin · Dinh Phung · Lennart Svensson · Juliano Tusi Amaral Laganá Pinto · Milad Alizadeh · Jianzhun Du · Kevin Murphy · Beatrix Benkő · Shashaank Vattikuti · Jonathan Gordon · Christopher Kanan · Sontje Ihler · Darin Graham · Michael Teng · Louis Kirsch · Tomas Pevny · Taras Holotyak -
2019 Workshop: Bayesian Deep Learning »
Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Eric Nalisnick · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2019 Poster: Bayesian Batch Active Learning as Sparse Subset Approximation »
Robert Pinsler · Jonathan Gordon · Eric Nalisnick · José Miguel Hernández-Lobato -
2019 Poster: Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model »
Wenbo Gong · Sebastian Tschiatschek · Sebastian Nowozin · Richard Turner · José Miguel Hernández-Lobato · Cheng Zhang -
2019 Poster: A Model to Search for Synthesizable Molecules »
John Bradshaw · Brooks Paige · Matt Kusner · Marwin Segler · José Miguel Hernández-Lobato -
2019 Poster: Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning »
David Janz · Jiri Hron · Przemysław Mazur · Katja Hofmann · José Miguel Hernández-Lobato · Sebastian Tschiatschek -
2018 Workshop: Machine Learning for Molecules and Materials »
José Miguel Hernández-Lobato · Klaus-Robert Müller · Brooks Paige · Matt Kusner · Stefan Chmiela · Kristof Schütt -
2018 : Poster spotlight session. »
Abdullah Salama · Wei-Cheng Chang · Aidan Gomez · Raphael Tang · FUXUN YU · Zhendong Zhang · Yuxin Zhang · Ji Lin · Stephen Tiedemann · Kun Bai · Sivaramakrishnan Sankarapandian · Marton Havasi · Jack Turner · Hsin-Pai Cheng · Yue Wang · Xiaofan Xu · Ruizhou Ding · Haoji Hu · Mohammad Shafiee · Christopher Blake · Chieh-Chi Kao · Daniel Kang · Yew Ken Chia · Amir Ashouri · Sourya Basu · Simon Wiedemann · Thorsten Laude -
2018 Workshop: Bayesian Deep Learning »
Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Andrew Wilson · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2018 Poster: Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo »
Marton Havasi · José Miguel Hernández-Lobato · Juan J. Murillo-Fuentes -
2017 Workshop: Bayesian Deep Learning »
Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Andrew Wilson · Andrew Wilson · Diederik Kingma · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2017 Workshop: Bayesian optimization for science and engineering »
Ruben Martinez-Cantin · José Miguel Hernández-Lobato · Javier Gonzalez -
2017 : Closing remarks »
José Miguel Hernández-Lobato -
2017 Workshop: Machine Learning for Molecules and Materials »
Kristof Schütt · Klaus-Robert Müller · Anatole von Lilienfeld · José Miguel Hernández-Lobato · Klaus-Robert Müller · Alan Aspuru-Guzik · Bharath Ramsundar · Matt Kusner · Brooks Paige · Stefan Chmiela · Alexandre Tkatchenko · Anatole von Lilienfeld · Koji Tsuda -
2016 : Panel Discussion »
Shakir Mohamed · David Blei · Ryan Adams · José Miguel Hernández-Lobato · Ian Goodfellow · Yarin Gal -
2016 : Automatic Chemical Design using Variational Autoencoders »
José Miguel Hernández-Lobato -
2016 : Alpha divergence minimization for Bayesian deep learning »
José Miguel Hernández-Lobato -
2015 Poster: Stochastic Expectation Propagation »
Yingzhen Li · José Miguel Hernández-Lobato · Richard Turner -
2015 Spotlight: Stochastic Expectation Propagation »
Yingzhen Li · José Miguel Hernández-Lobato · Richard Turner -
2014 Poster: Predictive Entropy Search for Efficient Global Optimization of Black-box Functions »
José Miguel Hernández-Lobato · Matthew Hoffman · Zoubin Ghahramani -
2014 Poster: Gaussian Process Volatility Model »
Yue Wu · José Miguel Hernández-Lobato · Zoubin Ghahramani -
2014 Spotlight: Predictive Entropy Search for Efficient Global Optimization of Black-box Functions »
José Miguel Hernández-Lobato · Matthew Hoffman · Zoubin Ghahramani -
2013 Poster: Learning Feature Selection Dependencies in Multi-task Learning »
Daniel Hernández-lobato · José Miguel Hernández-Lobato -
2013 Poster: Gaussian Process Conditional Copulas with Applications to Financial Time Series »
José Miguel Hernández-Lobato · James R Lloyd · Daniel Hernández-lobato -
2012 Poster: Collaborative Gaussian Processes for Preference Learning »
Neil Houlsby · José Miguel Hernández-Lobato · Ferenc Huszar · Zoubin Ghahramani -
2012 Poster: Semi-Supervised Domain Adaptation with Non-Parametric Copulas »
David Lopez-Paz · José Miguel Hernández-Lobato · Bernhard Schölkopf -
2012 Spotlight: Semi-Supervised Domain Adaptation with Non-Parametric Copulas »
David Lopez-Paz · José Miguel Hernández-Lobato · Bernhard Schölkopf -
2011 Poster: Robust Multi-Class Gaussian Process Classification »
Daniel Hernández-lobato · José Miguel Hernández-Lobato · Pierre Dupont -
2007 Poster: Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach »
José Miguel Hernández-Lobato · Tjeerd M Dijkstra · Tom Heskes