59 citations · 346 across the 15 of their papers we have counts for
15 papers
Good, Cheap, and Fast: Overfitted Image Compression with Wasserstein Distortion
Jona Ballé, Luca Versari, Emilien Dupont +2
Inspired by the success of generative image models, recent work on learned image compression increasingly focuses on better probabilistic models of the natural image distribution,…
C3: High-performance and low-complexity neural compression from a single image or video
Hyunjik Kim, Matthias Bauer, Lucas Theis +2
Most neural compression models are trained on large datasets of images or videos in order to generalize to unseen data. Such generalization typically requires large and expressive…
Deep Stochastic Processes via Functional Markov Transition Operators
Jin Xu, Emilien Dupont, Kaspar Märtens +2
We introduce Markov Neural Processes (MNPs), a new class of Stochastic Processes (SPs) which are constructed by stacking sequences of neural parameterised Markov transition operato…
Spatial Functa: Scaling Functa to ImageNet Classification and Generation
Matthias Bauer, Emilien Dupont, Andy Brock +3
Neural fields, also known as implicit neural representations, have emerged as a powerful means to represent complex signals of various modalities. Based on this Dupont et al. (2022…
COIN++: Neural Compression Across Modalities
Emilien Dupont, Hrushikesh Loya, Milad Alizadeh +3
Neural compression algorithms are typically based on autoencoders that require specialized encoder and decoder architectures for different data modalities. In this paper, we propos…
From data to functa: Your data point is a function and you can treat it like one
Emilien Dupont, Hyunjik Kim, S. M. Ali Eslami +2
It is common practice in deep learning to represent a measurement of the world on a discrete grid, e.g. a 2D grid of pixels. However, the underlying signal represented by these mea…
COIN: COmpression with Implicit Neural representations
Emilien Dupont, Adam Goliński, Milad Alizadeh +2
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 ima…
Generative Models as Distributions of Functions
Emilien Dupont, Yee Whye Teh, Arnaud Doucet
Generative models are typically trained on grid-like data such as images. As a result, the size of these models usually scales directly with the underlying grid resolution. In this…
LieTransformer: Equivariant self-attention for Lie Groups
Michael Hutchinson, Charline Le Lan, Sheheryar Zaidi +3
Group equivariant neural networks are used as building blocks of group invariant neural networks, which have been shown to improve generalisation performance and data efficiency th…
STEER: Simple Temporal Regularization For Neural ODEs
Arnab Ghosh, Harkirat Singh Behl, Emilien Dupont +2
Training Neural Ordinary Differential Equations (ODEs) is often computationally expensive. Indeed, computing the forward pass of such models involves solving an ODE which can becom…
Equivariant Neural Rendering
Emilien Dupont, Miguel Angel Bautista, Alex Colburn +4
We propose a framework for learning neural scene representations directly from images, without 3D supervision. Our key insight is that 3D structure can be imposed by ensuring that…
Augmented Neural ODEs
Emilien Dupont, Arnaud Doucet, Yee Whye Teh
We show that Neural Ordinary Differential Equations (ODEs) learn representations that preserve the topology of the input space and prove that this implies the existence of function…
Probabilistic Semantic Inpainting with Pixel Constrained CNNs
Emilien Dupont, Suhas Suresha
Semantic inpainting is the task of inferring missing pixels in an image given surrounding pixels and high level image semantics. Most semantic inpainting algorithms are determinist…
Learning Disentangled Joint Continuous and Discrete Representations
Emilien Dupont
We present a framework for learning disentangled and interpretable jointly continuous and discrete representations in an unsupervised manner. By augmenting the continuous latent di…
Generating Realistic Geology Conditioned on Physical Measurements with Generative Adversarial Networks
Emilien Dupont, Tuanfeng Zhang, Peter Tilke +2
An important problem in geostatistics is to build models of the subsurface of the Earth given physical measurements at sparse spatial locations. Typically, this is done using spati…