25 citations · 27 across the 5 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…