ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling | Paper Citation Record

Observation 11f5ed17-622c-48f7-9e2d-3d41a8833717 · outbound

This paper cites Statistics of natural image categories.Network: Com- putation in Neural Systems, 14(3):391, may 2003.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Statistics of natural image categories.Network: Com- putation in Neural Systems, 14(3):391, may 2003

Reference 1

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Observation 391a01ec-d44e-4d14-b82a-57f9720dea95 · outbound

This paper cites Adaptive IMLE for few-shot pretraining- free generative modelling.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Adaptive IMLE for few-shot pretraining- free generative modelling

Reference 2

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Observation 84f3c16c-306f-4d89-b390-5a3ce0e7d583 · outbound

This paper cites Discrete cosine transform.IEEE transactions on Computers, 100(1):90–93, 2006.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Discrete cosine transform.IEEE transactions on Computers, 100(1):90–93, 2006

Reference 3

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Observation b5b42f75-0e51-4b60-95fe-f0869945f685 · outbound

This paper cites Building Normalizing Flows with Stochastic Interpolants.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Building Normalizing Flows with Stochastic Interpolants

Reference 4

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Observation efe75644-d008-4381-99f9-f9b6ccdfa6b8 · outbound

This paper cites Towards Principled Methods for Training Generative Adversarial Networks.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Towards Principled Methods for Training Generative Adversarial Networks

Reference 5

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Observation e511a782-08c1-40b0-b516-e6ba2be95241 · outbound

This paper cites Wasserstein GAN.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Wasserstein GAN

Reference 6

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Observation 691ac40b-7b2c-4d6c-80b3-ceeb0230bee8 · outbound

This paper cites Multimodal Shape Completion via IMLE.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Multimodal Shape Completion via IMLE

Reference 7

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Observation 6005f82d-710d-4a10-9949-e3d8e4ef5ce4 · outbound

This paper cites A general and adaptive robust loss function.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling A general and adaptive robust loss function

Reference 8

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Observation ad43a032-9f64-4670-a73c-15d810c410b3 · outbound

This paper cites Seeing What a GAN Cannot Generate.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Seeing What a GAN Cannot Generate

Reference 9

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Observation 43613baa-2865-4332-80d3-bd312dfd7ba9 · outbound

This paper cites Large scale GAN training for high fidelity natural image synthesis.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Large scale GAN training for high fidelity natural image synthesis

Reference 10

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Observation bd9406db-002e-4d1e-aa9c-890ee3914e66 · outbound

This paper cites Emerging Properties in Self-Supervised Vision Transformers.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Emerging Properties in Self-Supervised Vision Transformers

Reference 11

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Observation b91acf67-af84-4add-a7be-3a70b684c095 · outbound

This paper cites Imagenet: A large- scale hierarchical image database.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Imagenet: A large- scale hierarchical image database

Reference 12

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Observation 450d9490-b9d0-4042-97e2-cc9e09730906 · outbound

This paper cites Diffusion Models Beat GANs on Image Synthesis.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Diffusion Models Beat GANs on Image Synthesis

Reference 13

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Observation b89ac3b7-ea7c-4610-b5f8-dee04f313b1e · outbound

This paper cites Diffusion is spectral autoregression, 2024.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Diffusion is spectral autoregression, 2024

Reference 14

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Observation a1bdcb80-b454-4371-ad63-01ba5e9352a4 · outbound

This paper cites Combating Mode Collapse in GAN training: An Empirical Analysis using Hessian Eigenvalues.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Combating Mode Collapse in GAN training: An Empirical Analysis using Hessian Eigenvalues

Reference 15

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Observation 74ff0aba-d205-4e4b-95bc-705846974be2 · outbound

This paper cites One Step Diffusion via Shortcut Models.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling One Step Diffusion via Shortcut Models

Reference 16

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Observation 92a8cf92-9e3d-41bd-a818-a533d6d1a56b · outbound

This paper cites Mean Flows for One-step Generative Modeling.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Mean Flows for One-step Generative Modeling

Reference 17

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Observation 43599174-dbe2-4658-a04a-2f941728ed42 · outbound

This paper cites Improved Mean Flows: On the Challenges of Fastforward Generative Models.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Improved Mean Flows: On the Challenges of Fastforward Generative Models

Reference 18

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Observation 42512350-7c25-489e-8426-2b055c9fa9c3 · outbound

This paper cites Generative Adversarial Networks.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Generative Adversarial Networks

Reference 19

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Observation d6de5409-a54d-44b0-b5cc-9ffc137cc7a4 · outbound

This paper cites An Undetectable Watermark for Generative Image Models.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling An Undetectable Watermark for Generative Image Models

Reference 20

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Observation b498cebd-aefe-4fbb-bfb8-aa3605e95941 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 21

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Observation 0842ea7a-56f7-4fa1-8777-06569600eaf8 · outbound

This paper cites Classifier-Free Diffusion Guidance.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Classifier-Free Diffusion Guidance

Reference 22

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Observation cf4c8991-4a07-485c-896b-a9cff506a878 · outbound

This paper cites Denoising Diffusion Probabilistic Models.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Denoising Diffusion Probabilistic Models

Reference 23

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Observation 6cf3586a-ba34-4b89-b698-e5ba79114344 · outbound

This paper cites Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

Reference 24

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Observation 818d7837-b6e0-458b-9e3d-f55da3f22bf7 · outbound

This paper cites Rethinking fid: Towards a better evaluation metric for image generation,.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Rethinking fid: Towards a better evaluation metric for image generation,

Reference 25

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Observation a9d97df0-d382-4fff-a147-726ee96ddecf · outbound

This paper cites Progressive growing of gans for improved quality, stability, and variation, 2018.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Progressive growing of gans for improved quality, stability, and variation, 2018

Reference 26

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Observation 0841ac7b-55dc-4ba8-af98-c8ec5ef291c6 · outbound

This paper cites A Style-Based Generator Architecture for Generative Adversarial Networks.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling A Style-Based Generator Architecture for Generative Adversarial Networks

Reference 27

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Observation 1cc07698-2d9b-4cb7-ad67-95f51fd895ec · outbound

This paper cites Elucidating the Design Space of Diffusion-Based Generative Models.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Elucidating the Design Space of Diffusion-Based Generative Models

Reference 28

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This paper cites Auto-Encoding Variational Bayes.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Auto-Encoding Variational Bayes

Reference 29

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This paper cites EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling

Reference 30

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Observation 214a04ed-969e-4c19-9e80-83fd8292b2a9 · outbound

This paper cites Learning multiple layers of features from tiny images.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Learning multiple layers of features from tiny images

Reference 31

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This paper cites Improved precision and recall metric for assessing generative models.Advances in Neural Information Processing Systems, 32, 2019.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Improved precision and recall metric for assessing generative models.Advances in Neural Information Processing Systems, 32, 2019

Reference 32

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This paper cites Implicit maximum likelihood estimation for real-time generative model predictive control, 2026.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Implicit maximum likelihood estimation for real-time generative model predictive control, 2026

Reference 33

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This paper cites Implicit Maximum Likelihood Estimation.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Implicit Maximum Likelihood Estimation

Reference 34

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This paper cites Flow Matching for Generative Modeling.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Flow Matching for Generative Modeling

Reference 35

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This paper cites A ConvNet for the 2020s.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling A ConvNet for the 2020s

Reference 36

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Observation 35e0854f-f64a-4461-9baf-b6dafdbe2cbe · outbound

This paper cites Decoupled Weight Decay Regularization.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Decoupled Weight Decay Regularization

Reference 37

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This paper cites Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers

Reference 38

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This paper cites Automated flower classification over a large number of classes.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Automated flower classification over a large number of classes

Reference 39

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This paper cites Styleformer: Transformer based Generative Adversarial Networks with Style Vector.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Styleformer: Transformer based Generative Adversarial Networks with Style Vector

Reference 40

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ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Scalable diffusion models with transformers

Reference 41

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This paper cites IMLE Policy: Fast and Sample Efficient Visuomotor Policy Learning via Implicit Maximum Likelihood Estimation.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling IMLE Policy: Fast and Sample Efficient Visuomotor Policy Learning via Implicit Maximum Likelihood Estimation

Reference 42

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This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling High-Resolution Image Synthesis with Latent Diffusion Models

Reference 43

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This paper cites Progressive distillation for fast sampling of diffusion models,.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Progressive distillation for fast sampling of diffusion models,

Reference 44

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This paper cites StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets

Reference 45

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ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Denoising Diffusion Implicit Models

Reference 46

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ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Score-Based Generative Modeling through Stochastic Differential Equations

Reference 47

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ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Consistency Models

Reference 48

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ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Unresolved cited work

Reference 49

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This paper cites Score-based generative modeling in latent space,.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Score-based generative modeling in latent space,

Reference 50

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This paper cites Rejection Sampling IMLE: Designing Priors for Better Few-Shot Image Synthesis.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Rejection Sampling IMLE: Designing Priors for Better Few-Shot Image Synthesis

Reference 51

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ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Unresolved cited work

Reference 52

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This paper cites Tackling the Generative Learning Trilemma with Denoising Diffusion GANs.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 53

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ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling StyleSwin: Transformer-based GAN for High-resolution Image Generation

Reference 54

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This paper cites The Unreasonable Effectiveness of Deep Features as a Perceptual Metric.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

Reference 55

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ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Inductive moment matching

Reference 56

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ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Score-based Generative Modeling in Latent Space

Reference 2021

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Observation d6fdda23-b2c1-4354-934d-0f1a3ff48359 · outbound

This paper cites Progressive Distillation for Fast Sampling of Diffusion Models.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Progressive Distillation for Fast Sampling of Diffusion Models

Reference 2022

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Observation a3bfe974-6d55-4620-8b03-5e0d9b10fd28 · outbound

This paper cites Rethinking FID: Towards a Better Evaluation Metric for Image Generation.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Rethinking FID: Towards a Better Evaluation Metric for Image Generation

Reference 2024

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Coverage vector

Reference resolution

Outbound references

Pith citing papers