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Deep Generative Modelling : A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models

Deep generative models are a class of techniques that train deep neural networks to model the distribution of training samples. Research has fragmented into various interconnected approaches, each of which make trade-offs including run-time, diversity, and architectural restrictions. In particular, this compendium covers energy-based models, variational autoencoders, generative adversarial networks, autoregressive models, normalizing flows, in addition to numerous hybrid approaches. These techniques are compared and contrasted, explaining the premises behind each and how they are interrelated, while reviewing current state-of-the-art advances and implementations.

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.44 No.11 Part 1 November 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 7327-7347

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

AUTOREGRESSIVE MODELS
GENERATIVE MODELS
GENERATIVE ADVERSARIAL NETWORKS
ENERGY-BASED MODELS
NORMALIZING FLOWS
DEEP LEARNING
VARIATIONAL AUTOENCODERS

Info Detail Spesifik

DOI: 10.1109/TPAMI.2021.3116668

Pernyataan Tanggungjawab

Sam Bond-Taylor, Adam Leach, Yang Long, Chris G. Willcocks

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