Generative Models as Distributions of Functions

Autor:	Dupont, E, Teh, YW, Doucet, A
Rok vydání:	2021
Předmět:	FOS: Computer and information sciences Computer Science - Machine Learning Statistics - Machine Learning Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Machine Learning (stat.ML) Machine Learning (cs.LG)
DOI:	10.48550/arxiv.2102.04776
Popis:	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 paper, we abandon discretized grids and instead parameterize individual data points by continuous functions. We then build generative models by learning distributions over such functions. By treating data points as functions, we can abstract away from the specific type of data we train on and construct models that are agnostic to discretization. To train our model, we use an adversarial approach with a discriminator that acts on continuous signals. Through experiments on a wide variety of data modalities including images, 3D shapes and climate data, we demonstrate that our model can learn rich distributions of functions independently of data type and resolution. Comment: AISTATS 2022 Oral camera ready. Incorporated reviewer feedback
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dcbea73d890162635b560432a994e213 Zobrazit plný text záznamu