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Found 23 papers, 13 papers with code
HexaConv
We find that, due to the reduced anisotropy of hexagonal filters, planar HexaConv provides better accuracy than planar convolution with square filters, given a fixed parameter budget.
Emerging Convolutions for Generative Normalizing Flows
We generalize the 1 x 1 convolutions proposed in Glow to invertible d x d convolutions, which are more flexible since they operate on both channel and spatial axes.
Integer Discrete Flows and Lossless Compression
For that reason, we introduce a flow-based generative model for ordinal discrete data called Integer Discrete Flow (IDF): a bijective integer map that can learn rich transformations on high-dimensional data.
Learning Likelihoods with Conditional Normalizing Flows
Normalizing Flows (NFs) are able to model complicated distributions p(y) with strong inter-dimensional correlations and high multimodality by transforming a simple base density p(z) through an invertible neural network under the change of variables formula.
Learning Discrete Distributions by Dequantization
Media is generally stored digitally and is therefore discrete.
Predictive Sampling with Forecasting Autoregressive Models
Autoregressive models (ARMs) currently hold state-of-the-art performance in likelihood-based modeling of image and audio data.
The Convolution Exponential and Generalized Sylvester Flows
Empirically, we show that the convolution exponential outperforms other linear transformations in generative flows on CIFAR10 and the graph convolution exponential improves the performance of graph normalizing flows.
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows
Normalizing flows and variational autoencoders are powerful generative models that can represent complicated density functions.
Self Normalizing Flows
Efficient gradient computation of the Jacobian determinant term is a core problem in many machine learning settings, and especially so in the normalizing flow framework.
Argmax Flows: Learning Categorical Distributions with Normalizing Flows
This paper introduces a new method to define and train continuous distributions such as normalizing flows directly on categorical data, for example text and image segmentation.
Variational Determinant Estimation with Spherical Normalizing Flows
This paper introduces the Variational Determinant Estimator (VDE), a variational extension of the recently proposed determinant estimator discovered by arXiv:2005. 06553v2.
Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions
Argmax Flows are defined by a composition of a continuous distribution (such as a normalizing flow), and an argmax function.
E(n) Equivariant Graph Neural Networks
This paper introduces a new model to learn graph neural networks equivariant to rotations, translations, reflections and permutations called E(n)-Equivariant Graph Neural Networks (EGNNs).
E(n) Equivariant Normalizing Flows
This paper introduces a generative model equivariant to Euclidean symmetries: E(n) Equivariant Normalizing Flows (E-NFs).
Discrete Denoising Flows
Discrete flow-based models are a recently proposed class of generative models that learn invertible transformations for discrete random variables.
Autoregressive Diffusion Models
We introduce Autoregressive Diffusion Models (ARDMs), a model class encompassing and generalizing order-agnostic autoregressive models (Uria et al., 2014) and absorbing discrete diffusion (Austin et al., 2021), which we show are special cases of ARDMs under mild assumptions.
Ranked #8 on
Image Generation
on CIFAR-10
(bits/dimension metric)
Equivariant Diffusion for Molecule Generation in 3D
This work introduces a diffusion model for molecule generation in 3D that is equivariant to Euclidean transformations.
Blurring Diffusion Models
Recently, Rissanen et al., (2022) have presented a new type of diffusion process for generative modeling based on heat dissipation, or blurring, as an alternative to isotropic Gaussian diffusion.
Simple diffusion: End-to-end diffusion for high resolution images
Currently, applying diffusion models in pixel space of high resolution images is difficult.
Ranked #4 on
Conditional Image Generation
on ImageNet 128x128
High-Fidelity Image Compression with Score-based Generative Models
Despite the tremendous success of diffusion generative models in text-to-image generation, replicating this success in the domain of image compression has proven difficult.
DORSal: Diffusion for Object-centric Representations of Scenes et al
In this paper, we leverage recent progress in diffusion models to equip 3D scene representation learning models with the ability to render high-fidelity novel views, while retaining benefits such as object-level scene editing to a large degree.
Rolling Diffusion Models
Diffusion models have recently been increasingly applied to temporal data such as video, fluid mechanics simulations, or climate data.
Multistep Consistency Models
By increasing the sample budget from a single step to 2-8 steps, we can train models more easily that generate higher quality samples, while retaining much of the sampling speed benefits.
