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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 …
the distribution of training samples. Research has fragmented into various interconnected …
Recent advances for quantum neural networks in generative learning
Quantum computers are next-generation devices that hold promise to perform calculations
beyond the reach of classical computers. A leading method towards achieving this goal is …
beyond the reach of classical computers. A leading method towards achieving this goal is …
Geometric latent diffusion models for 3d molecule generation
Generative models, especially diffusion models (DMs), have achieved promising results for
generating feature-rich geometries and advancing foundational science problems such as …
generating feature-rich geometries and advancing foundational science problems such as …
High-resolution image synthesis with latent diffusion models
By decomposing the image formation process into a sequential application of denoising
autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image …
autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image …
Score-based generative modeling in latent space
Score-based generative models (SGMs) have recently demonstrated impressive results in
terms of both sample quality and distribution coverage. However, they are usually applied …
terms of both sample quality and distribution coverage. However, they are usually applied …
Taming transformers for high-resolution image synthesis
Designed to learn long-range interactions on sequential data, transformers continue to show
state-of-the-art results on a wide variety of tasks. In contrast to CNNs, they contain no …
state-of-the-art results on a wide variety of tasks. In contrast to CNNs, they contain no …
Very deep vaes generalize autoregressive models and can outperform them on images
We present a hierarchical VAE that, for the first time, generates samples quickly while
outperforming the PixelCNN in log-likelihood on all natural image benchmarks. We begin by …
outperforming the PixelCNN in log-likelihood on all natural image benchmarks. We begin by …
Disentangled and controllable face image generation via 3d imitative-contrastive learning
We propose an approach for face image generation of virtual people with disentangled,
precisely-controllable latent representations for identity of non-existing people, expression …
precisely-controllable latent representations for identity of non-existing people, expression …
Imagebart: Bidirectional context with multinomial diffusion for autoregressive image synthesis
Autoregressive models and their sequential factorization of the data likelihood have recently
demonstrated great potential for image representation and synthesis. Nevertheless, they …
demonstrated great potential for image representation and synthesis. Nevertheless, they …
D2c: Diffusion-decoding models for few-shot conditional generation
Conditional generative models of high-dimensional images have many applications, but
supervision signals from conditions to images can be expensive to acquire. This paper …
supervision signals from conditions to images can be expensive to acquire. This paper …