Paper Citation Record · LEDGER

The GAN is dead; long live the GAN! A Modern GAN Baseline

As of 15 August 2026, this Paper Citation Record lists 100 of 114 outbound references and 4 inbound Pith citation observations for arXiv:2501.05441.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.05441 v1

Coverage vector

measured 100 of 114 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:18:53.006403Z

measured 104 of 104 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-24T00:59:59.607299Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

100 of 114 outbound references displayed

verified exact2
verified fuzzy30
unresolved67
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

4
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 5ec66526-6701-413c-97eb-791fcbf46429 · outbound

This paper cites Layer Normalization.

The GAN is dead; long live the GAN! A Modern GAN Baseline Layer Normalization

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.571312Z

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Observation 3e0c7582-e992-4e07-85e8-a10f965837c1 · outbound

This paper cites SMU: smooth activation function for deep networks using smoothing maximum technique.

The GAN is dead; long live the GAN! A Modern GAN Baseline SMU: smooth activation function for deep networks using smoothing maximum technique

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.576172Z

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Observation f585d711-66b3-462b-bdc5-167eedb8534b · outbound

This paper cites Large Scale GAN Training for High Fidelity Natural Image Synthesis.

The GAN is dead; long live the GAN! A Modern GAN Baseline Large Scale GAN Training for High Fidelity Natural Image Synthesis

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.581202Z

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Observation 43c2b1c2-c67d-4c73-a2b0-d2a0771606cb · outbound

This paper cites High-performance large-scale image recognition without normalization.

The GAN is dead; long live the GAN! A Modern GAN Baseline High-performance large-scale image recognition without normalization

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.586238Z

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Observation b1df7363-d697-4261-9593-5eefec80a55a · outbound

This paper cites Xception: Deep learning with depthwise separable convolutions.

The GAN is dead; long live the GAN! A Modern GAN Baseline Xception: Deep learning with depthwise separable convolutions

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.590713Z

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Observation 1be61f66-61cd-4a87-807b-cf03c65a45af · outbound

This paper cites A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets.

The GAN is dead; long live the GAN! A Modern GAN Baseline A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.595047Z

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Observation cedb0520-56e3-4153-bbff-125a3c08cc1b · outbound

This paper cites Diffusion models beat gans on image synthesis.

The GAN is dead; long live the GAN! A Modern GAN Baseline Diffusion models beat gans on image synthesis

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.599663Z

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Observation 05ff86af-1879-4103-b7f8-0362cdc7e8c6 · outbound

This paper cites Prescribed Generative Adversarial Networks.

The GAN is dead; long live the GAN! A Modern GAN Baseline Prescribed Generative Adversarial Networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.603396Z

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Observation d437be79-f5d4-43c1-94d0-f3360f08b875 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

The GAN is dead; long live the GAN! A Modern GAN Baseline An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.607626Z

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Observation bebba5fa-9a60-47c3-a4cf-b8b147cdee13 · outbound

This paper cites DigGAN: Discriminator gradient gap regularization for GAN training with limited data.

The GAN is dead; long live the GAN! A Modern GAN Baseline DigGAN: Discriminator gradient gap regularization for GAN training with limited data

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.612128Z

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Observation 716ee5c5-dbcd-499e-b5d4-34d39504ecfb · outbound

This paper cites Negative momentum for improved game dynamics.

The GAN is dead; long live the GAN! A Modern GAN Baseline Negative momentum for improved game dynamics

Reference 11

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Observation 444f3057-d877-4d4c-a190-6e54b23d94b3 · outbound

This paper cites Commoncanvas: Open diffusion models trained on creative-commons images.

The GAN is dead; long live the GAN! A Modern GAN Baseline Commoncanvas: Open diffusion models trained on creative-commons images

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.621224Z

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Observation 73b90400-12e3-4e96-a9dd-fc3dd3003c3b · outbound

This paper cites Generative adversarial networks.

The GAN is dead; long live the GAN! A Modern GAN Baseline Generative adversarial networks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.625886Z

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Observation 4cc678f7-0925-4559-9c5a-e0e290e56922 · outbound

This paper cites Improved training of wasserstein gans.

The GAN is dead; long live the GAN! A Modern GAN Baseline Improved training of wasserstein gans

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.630012Z

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Observation c2a6479e-cb25-497d-a54f-1ec82a7ddce9 · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.

The GAN is dead; long live the GAN! A Modern GAN Baseline Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.634104Z

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Observation dac99fe5-b913-4f12-aded-ba45cf60a6eb · outbound

This paper cites Deep residual learning for image recognition.

The GAN is dead; long live the GAN! A Modern GAN Baseline Deep residual learning for image recognition

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.638092Z

Source-reported events for the cited work

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Observation c4c99005-e111-4276-94f8-99954190fcf0 · outbound

This paper cites Identity mappings in deep residual networks.

The GAN is dead; long live the GAN! A Modern GAN Baseline Identity mappings in deep residual networks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.642355Z

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Observation 385cb5dd-39bc-4b43-bb74-c13e5137d6be · outbound

This paper cites Gaussian Error Linear Units (GELUs).

The GAN is dead; long live the GAN! A Modern GAN Baseline Gaussian Error Linear Units (GELUs)

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.646802Z

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Observation df4205be-e226-44b9-8898-fe7e040e2d0b · outbound

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

The GAN is dead; long live the GAN! A Modern GAN Baseline Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.650905Z

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Observation 306d3102-04d4-4658-8442-9910756a1772 · outbound

This paper cites Denoising diffusion probabilistic models.

The GAN is dead; long live the GAN! A Modern GAN Baseline Denoising diffusion probabilistic models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.655717Z

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Observation 0376b0bc-1a4d-41a0-ba63-8dc2b26dee52 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift.

The GAN is dead; long live the GAN! A Modern GAN Baseline Batch normalization: Accelerating deep network training by reducing internal covariate shift

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.660293Z

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Observation ecbc403f-8575-4b00-8433-4e35b6373908 · outbound

This paper cites The relativistic discriminator: a key element missing from standard GAN.

The GAN is dead; long live the GAN! A Modern GAN Baseline The relativistic discriminator: a key element missing from standard GAN

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.664553Z

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Observation f4081727-3cbc-4d45-8e0d-9f3ad6b46734 · outbound

This paper cites Gradient penalty from a maximum margin perspective.

The GAN is dead; long live the GAN! A Modern GAN Baseline Gradient penalty from a maximum margin perspective

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-10T21:18:53.392662Z

Source-reported events for the cited work

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Observation 7a6cc956-57a3-4100-bed4-b3e9250bda4e · outbound

This paper cites Adversarial score matching and improved sampling for image generation.

The GAN is dead; long live the GAN! A Modern GAN Baseline Adversarial score matching and improved sampling for image generation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.674135Z

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Observation ecbd3828-8e9d-42bc-b9ff-98960f975259 · outbound

This paper cites Studiogan: a taxonomy and benchmark of gans for image synthesis.

The GAN is dead; long live the GAN! A Modern GAN Baseline Studiogan: a taxonomy and benchmark of gans for image synthesis

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.678595Z

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Observation e6ef636e-6615-466e-9716-32d9670c60e0 · outbound

This paper cites Scaling up gans for text-to-image synthesis.

The GAN is dead; long live the GAN! A Modern GAN Baseline Scaling up gans for text-to-image synthesis

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.682642Z

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Observation 76c93997-e228-420b-9fca-ce0d98546470 · outbound

This paper cites Msg-gan: Multi-scale gradients for generative adversarial networks.

The GAN is dead; long live the GAN! A Modern GAN Baseline Msg-gan: Multi-scale gradients for generative adversarial networks

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.686462Z

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Observation fcbf82e6-fd46-4cb4-a839-a5e8e7dbeaa1 · outbound

This paper cites Progressive Growing of GANs for Improved Quality, Stability, and Variation.

The GAN is dead; long live the GAN! A Modern GAN Baseline Progressive Growing of GANs for Improved Quality, Stability, and Variation

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:52.690184Z

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Observation bb06b5a8-c208-4e12-95e7-3efe6a482ca6 · outbound

This paper cites A style-based generator architecture for generative adversarial networks.

The GAN is dead; long live the GAN! A Modern GAN Baseline A style-based generator architecture for generative adversarial networks

Reference 29

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Observation 5357342a-7bf1-4fc9-a840-dab3f54d62c3 · outbound

This paper cites Training generative adversarial networks with limited data.

The GAN is dead; long live the GAN! A Modern GAN Baseline Training generative adversarial networks with limited data

Reference 30

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Observation 8072b655-1e58-4781-a8a0-884cbc4c9801 · outbound

This paper cites Analyzing and improving the image quality of stylegan.

The GAN is dead; long live the GAN! A Modern GAN Baseline Analyzing and improving the image quality of stylegan

Reference 31

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Observation 8364b156-157f-480a-b0c3-66bcf434ee43 · outbound

This paper cites Alias-free generative adversarial networks.

The GAN is dead; long live the GAN! A Modern GAN Baseline Alias-free generative adversarial networks

Reference 32

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Observation f835bbf8-118f-411a-8467-217e26353b40 · outbound

This paper cites Elucidating the design space of diffusion-based generative models.

The GAN is dead; long live the GAN! A Modern GAN Baseline Elucidating the design space of diffusion-based generative models

Reference 33

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Observation 76b7d090-7429-44e9-90f9-8209b8e4d317 · outbound

This paper cites Analyzing and Improving the Training Dynamics of Diffusion Models.

The GAN is dead; long live the GAN! A Modern GAN Baseline Analyzing and Improving the Training Dynamics of Diffusion Models

Reference 34

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Observation e1a1642e-fc47-4c43-81d3-133549cfe04b · outbound

This paper cites Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation.

The GAN is dead; long live the GAN! A Modern GAN Baseline Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation

Reference 35

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Observation e8f9e894-c60e-46ea-9103-9d66216eab59 · outbound

This paper cites Variational diffusion models.

The GAN is dead; long live the GAN! A Modern GAN Baseline Variational diffusion models

Reference 36

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Observation 5f287e93-6a51-4f1f-b727-f404edc41043 · outbound

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

The GAN is dead; long live the GAN! A Modern GAN Baseline Learning multiple layers of features from tiny images

Reference 37

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Observation 0b38b6ca-4029-4372-a400-0c0f3b6c42ab · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

The GAN is dead; long live the GAN! A Modern GAN Baseline Imagenet classification with deep convolutional neural networks

Reference 38

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Observation f82bdfde-441f-417a-a178-e7fbe62cbd01 · outbound

This paper cites Maximum Entropy Generators for Energy-Based Models.

The GAN is dead; long live the GAN! A Modern GAN Baseline Maximum Entropy Generators for Energy-Based Models

Reference 39

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This paper cites Improved precision and recall metric for assessing generative models.

The GAN is dead; long live the GAN! A Modern GAN Baseline Improved precision and recall metric for assessing generative models

Reference 40

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Observation 99b13d68-adc8-4929-aea2-435c67e418f5 · outbound

This paper cites The Role of ImageNet Classes in Fr\'echet Inception Distance.

The GAN is dead; long live the GAN! A Modern GAN Baseline The Role of ImageNet Classes in Fr\'echet Inception Distance

Reference 41

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Observation 071a9a3b-3ca0-43ac-a10f-db720ed661cb · outbound

This paper cites ViTGAN: Training GANs with Vision Transformers.

The GAN is dead; long live the GAN! A Modern GAN Baseline ViTGAN: Training GANs with Vision Transformers

Reference 42

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This paper cites Enhanced deep residual networks for single image super-resolution.

The GAN is dead; long live the GAN! A Modern GAN Baseline Enhanced deep residual networks for single image super-resolution

Reference 43

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This paper cites Geometric GAN.

The GAN is dead; long live the GAN! A Modern GAN Baseline Geometric GAN

Reference 44

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This paper cites Anycost gans for interactive image synthesis and editing.

The GAN is dead; long live the GAN! A Modern GAN Baseline Anycost gans for interactive image synthesis and editing

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This paper cites Pacgan: The power of two samples in generative adversarial networks.

The GAN is dead; long live the GAN! A Modern GAN Baseline Pacgan: The power of two samples in generative adversarial networks

Reference 46

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Observation 4f007a0c-1bd2-498e-b2e5-95adfe0cf927 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

The GAN is dead; long live the GAN! A Modern GAN Baseline Swin transformer: Hierarchical vision transformer using shifted windows

Reference 47

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This paper cites A convnet for the 2020s.

The GAN is dead; long live the GAN! A Modern GAN Baseline A convnet for the 2020s

Reference 48

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This paper cites Compensation Sampling for Improved Convergence in Diffusion Models.

The GAN is dead; long live the GAN! A Modern GAN Baseline Compensation Sampling for Improved Convergence in Diffusion Models

Reference 49

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This paper cites Least squares generative adversarial networks.

The GAN is dead; long live the GAN! A Modern GAN Baseline Least squares generative adversarial networks

Reference 50

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This paper cites The numerics of gans.

The GAN is dead; long live the GAN! A Modern GAN Baseline The numerics of gans

Reference 51

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This paper cites Which training methods for gans do actually converge? In International conference on machine learning, pp.

The GAN is dead; long live the GAN! A Modern GAN Baseline Which training methods for gans do actually converge? In International conference on machine learning, pp

Reference 52

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Observation e1562513-a4b6-4e9e-8107-ebbc40f8aad4 · outbound

This paper cites Unrolled generative adversarial networks.

The GAN is dead; long live the GAN! A Modern GAN Baseline Unrolled generative adversarial networks

Reference 53

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Observation 4690b842-bb5f-425d-afc2-f0b78d02eb0f · outbound

This paper cites cGANs with Projection Discriminator.

The GAN is dead; long live the GAN! A Modern GAN Baseline cGANs with Projection Discriminator

Reference 54

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Observation 7b757a12-dbca-47a6-b7fb-bfecf745a406 · outbound

This paper cites Gradient descent gan optimization is locally stable.

The GAN is dead; long live the GAN! A Modern GAN Baseline Gradient descent gan optimization is locally stable

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:18:54.089342Z

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Observation 75cdc4d5-55d1-4fd9-95a3-34dce22b491d · outbound

This paper cites Input Perturbation Reduces Exposure Bias in Diffusion Models.

The GAN is dead; long live the GAN! A Modern GAN Baseline Input Perturbation Reduces Exposure Bias in Diffusion Models

Reference 56

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Observation fda3b4ff-54e6-412e-b031-9c845b13e3e2 · outbound

This paper cites f-gan: Training generative neural samplers using variational divergence minimization.

The GAN is dead; long live the GAN! A Modern GAN Baseline f-gan: Training generative neural samplers using variational divergence minimization

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:18:54.077453Z

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Observation 1e274123-e76f-4d10-9c22-112ae67e9ec1 · outbound

This paper cites Scalable diffusion models with transformers.

The GAN is dead; long live the GAN! A Modern GAN Baseline Scalable diffusion models with transformers

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:18:54.064446Z

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Observation eabafb4e-53ed-475a-9b73-eac9734fb2f5 · outbound

This paper cites Diffusion autoencoders: Toward a meaningful and decodable representation.

The GAN is dead; long live the GAN! A Modern GAN Baseline Diffusion autoencoders: Toward a meaningful and decodable representation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:18:54.051267Z

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Observation 9cad2cac-cdaa-4ca2-af02-2e383c082485 · outbound

This paper cites Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.

The GAN is dead; long live the GAN! A Modern GAN Baseline Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Reference 60

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Observation b5281f08-4576-4a2d-8ab5-b49ea7cc3cdb · outbound

This paper cites Searching for Activation Functions.

The GAN is dead; long live the GAN! A Modern GAN Baseline Searching for Activation Functions

Reference 61

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Observation d9e7b036-3368-435a-9e64-4ad347399bcf · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

The GAN is dead; long live the GAN! A Modern GAN Baseline High-resolution image synthesis with latent diffusion models

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:18:54.039725Z

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Observation 2e4c1309-a63e-4e57-b8fd-985f2093f055 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

The GAN is dead; long live the GAN! A Modern GAN Baseline U-net: Convolutional networks for biomedical image segmentation

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:18:54.025816Z

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Observation c7f051e5-1e25-4eda-bd9e-aa20878961d0 · outbound

This paper cites Stabilizing training of generative adversarial networks through regularization.

The GAN is dead; long live the GAN! A Modern GAN Baseline Stabilizing training of generative adversarial networks through regularization

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:18:54.011103Z

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Observation 089bf63c-0803-48f9-95e7-bbafae130a23 · outbound

This paper cites LiteVAE: Lightweight and Efficient Variational Autoencoders for Latent Diffusion Models.

The GAN is dead; long live the GAN! A Modern GAN Baseline LiteVAE: Lightweight and Efficient Variational Autoencoders for Latent Diffusion Models

Reference 65

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Observation 38e4397f-fb6e-4534-9b83-34c87a76a8c4 · outbound

This paper cites Diffusion Models With Learned Adaptive Noise.

The GAN is dead; long live the GAN! A Modern GAN Baseline Diffusion Models With Learned Adaptive Noise

Reference 66

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Observation f9f30163-8de7-4859-8606-0ca6f20f2564 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

The GAN is dead; long live the GAN! A Modern GAN Baseline Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 67

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Observation 67fd3164-e053-4472-8160-68700a7b4bd2 · outbound

This paper cites Projected gans converge faster.

The GAN is dead; long live the GAN! A Modern GAN Baseline Projected gans converge faster

Reference 68

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Observation 7efabafb-4f9b-4592-9b1e-35e81c92429e · outbound

This paper cites StyleGAN-XL: Scaling stylegan to large diverse datasets.

The GAN is dead; long live the GAN! A Modern GAN Baseline StyleGAN-XL: Scaling stylegan to large diverse datasets

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:18:53.973254Z

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Observation dd0625f8-166b-448f-bc47-1c969e4b5875 · outbound

This paper cites Stylegan-t: Unlocking the power of gans for fast large-scale text-to-image synthesis.

The GAN is dead; long live the GAN! A Modern GAN Baseline Stylegan-t: Unlocking the power of gans for fast large-scale text-to-image synthesis

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:18:53.958416Z

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Observation 1570626f-5495-42f1-9fb4-c1098f43b914 · outbound

This paper cites Real-time single image and video super-resolution using an efficient sub- pixel convolutional neural network.

The GAN is dead; long live the GAN! A Modern GAN Baseline Real-time single image and video super-resolution using an efficient sub- pixel convolutional neural network

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:18:53.944116Z

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Observation 5a8d61fe-ec09-4f8a-ac60-05f295cf40f4 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

The GAN is dead; long live the GAN! A Modern GAN Baseline Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 73

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Observation 3182661f-1070-46f9-9366-c08c2db5aff9 · outbound

This paper cites Polynomial implicit neural representations for large diverse datasets.

The GAN is dead; long live the GAN! A Modern GAN Baseline Polynomial implicit neural representations for large diverse datasets

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:18:53.929784Z

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Observation 6fdb6542-45d4-4c1c-aa5c-7334c6cc3e02 · outbound

This paper cites Amortised MAP Inference for Image Super-resolution.

The GAN is dead; long live the GAN! A Modern GAN Baseline Amortised MAP Inference for Image Super-resolution

Reference 75

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Observation 87433cf0-401b-45b7-b636-f5a9c6427d34 · outbound

This paper cites Denoising diffusion implicit models.

The GAN is dead; long live the GAN! A Modern GAN Baseline Denoising diffusion implicit models

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:18:53.916268Z

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Observation bf4f90ab-bbcb-490a-ab5b-1c27a4f26277 · outbound

This paper cites Improved techniques for training consistency models.

The GAN is dead; long live the GAN! A Modern GAN Baseline Improved techniques for training consistency models

Reference 77

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Observation 90ab72f1-2cb0-448c-84ff-f44c5a5076ef · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

The GAN is dead; long live the GAN! A Modern GAN Baseline Score-Based Generative Modeling through Stochastic Differential Equations

Reference 78

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Observation e863171b-1258-47e8-9f2c-583bf51d5474 · outbound

This paper cites Consistency models.

The GAN is dead; long live the GAN! A Modern GAN Baseline Consistency models

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:18:53.895480Z

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Observation 23411b5c-4297-4cbc-b2c5-dab5e3fc115b · outbound

This paper cites Veegan: Reducing mode collapse in gans using implicit variational learning.

The GAN is dead; long live the GAN! A Modern GAN Baseline Veegan: Reducing mode collapse in gans using implicit variational learning

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:18:53.883022Z

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Observation 3be2d058-8be1-48d9-87f7-14adf2c405b7 · outbound

This paper cites Towards a better global loss landscape of gans.

The GAN is dead; long live the GAN! A Modern GAN Baseline Towards a better global loss landscape of gans

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:18:53.871532Z

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Observation 7b6cdaa5-a38e-41b0-beb3-515298bbfffd · outbound

This paper cites SAN: Inducing metrizability of GAN with discriminative normalized linear layer.

The GAN is dead; long live the GAN! A Modern GAN Baseline SAN: Inducing metrizability of GAN with discriminative normalized linear layer

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:18:53.858305Z

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Observation 6f81013e-3e1b-442e-8f8e-8ed900a5a027 · outbound

This paper cites Alleviation of gradient exploding in gans: Fake can be real.

The GAN is dead; long live the GAN! A Modern GAN Baseline Alleviation of gradient exploding in gans: Fake can be real

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:18:53.844394Z

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Observation 000ce858-53ba-48bc-8e85-56752e5c02f3 · outbound

This paper cites Improving generalization and stability of generative adversarial networks.

The GAN is dead; long live the GAN! A Modern GAN Baseline Improving generalization and stability of generative adversarial networks

Reference 84

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Observation 051da960-a001-4fe5-b734-25c6ed3ae796 · outbound

This paper cites Instance Normalization: The Missing Ingredient for Fast Stylization.

The GAN is dead; long live the GAN! A Modern GAN Baseline Instance Normalization: The Missing Ingredient for Fast Stylization

Reference 85

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Observation 223c23f1-f695-4dd8-bf93-47710e3012ab · outbound

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

The GAN is dead; long live the GAN! A Modern GAN Baseline Score-based generative modeling in latent space

Reference 86

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Observation 8124e5ac-3905-4938-b15a-a8e21b72acc9 · outbound

This paper cites Attention is all you need.

The GAN is dead; long live the GAN! A Modern GAN Baseline Attention is all you need

Reference 87

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Observation 361c8047-99d8-45ab-a051-3c9b87c4159b · outbound

This paper cites Esrgan: Enhanced super-resolution generative adversarial networks.

The GAN is dead; long live the GAN! A Modern GAN Baseline Esrgan: Enhanced super-resolution generative adversarial networks

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:18:53.803153Z

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Observation 43a6b00b-2617-4a46-889d-5d175ef2d8b7 · outbound

This paper cites Infodiffusion: Representation learning using information maximizing diffusion models.

The GAN is dead; long live the GAN! A Modern GAN Baseline Infodiffusion: Representation learning using information maximizing diffusion models

Reference 89

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Observation 522d14b6-0a17-4893-bf74-9bb1044ab258 · outbound

This paper cites Diffusion-gan: Training gans with diffusion.

The GAN is dead; long live the GAN! A Modern GAN Baseline Diffusion-gan: Training gans with diffusion

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:18:53.780011Z

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Observation bd00c78a-d78a-4560-b081-6e402ef9ea19 · outbound

This paper cites Group normalization.

The GAN is dead; long live the GAN! A Modern GAN Baseline Group normalization

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:18:53.767829Z

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Observation 1b83fe17-e8cb-4bed-8be3-7cbefaae63b3 · outbound

This paper cites VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models.

The GAN is dead; long live the GAN! A Modern GAN Baseline VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models

Reference 93

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Observation 1df595da-9ab9-4bb4-9159-330b3f7ecdba · outbound

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

The GAN is dead; long live the GAN! A Modern GAN Baseline Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 94

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Observation 50d482ba-d493-4afd-90d0-d8ca99d59e35 · outbound

This paper cites Aggregated residual transforma- tions for deep neural networks.

The GAN is dead; long live the GAN! A Modern GAN Baseline Aggregated residual transforma- tions for deep neural networks

Reference 95

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Observation 888162b1-daaf-4d05-86de-68b586d9989d · outbound

This paper cites One-step diffusion with distribution matching distillation.

The GAN is dead; long live the GAN! A Modern GAN Baseline One-step diffusion with distribution matching distillation

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:18:53.740757Z

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Observation 847bd1d5-d9b1-4fc3-aa32-08886ae3cf73 · outbound

This paper cites Metaformer is actually what you need for vision.

The GAN is dead; long live the GAN! A Modern GAN Baseline Metaformer is actually what you need for vision

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:18:53.728666Z

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Observation 6261dfa7-7740-41ec-877b-5f23abfbce1d · outbound

This paper cites Styleswin: Transformer-based gan for high-resolution image generation.

The GAN is dead; long live the GAN! A Modern GAN Baseline Styleswin: Transformer-based gan for high-resolution image generation

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:18:53.717936Z

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Observation 340b5042-6a7f-44a8-81ab-bae16396ce6c · outbound

This paper cites Fixup Initialization: Residual Learning Without Normalization.

The GAN is dead; long live the GAN! A Modern GAN Baseline Fixup Initialization: Residual Learning Without Normalization

Reference 99

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Observation 8cfd6da4-f400-472b-9459-a70fc371b8cf · outbound

This paper cites Making convolutional networks shift-invariant again.

The GAN is dead; long live the GAN! A Modern GAN Baseline Making convolutional networks shift-invariant again

Reference 100

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Observation fb8002a3-0a8d-4790-b496-ce085957d76e · outbound

This paper cites Improved consistency regularization for gans.

The GAN is dead; long live the GAN! A Modern GAN Baseline Improved consistency regularization for gans

Reference 101

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Observation 523d642a-ff51-4d0e-b857-7711aa036ad0 · outbound

This paper cites Claim of convergence properties is justified in Appendices A,B,C.

The GAN is dead; long live the GAN! A Modern GAN Baseline Claim of convergence properties is justified in Appendices A,B,C

Reference 102

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:18:53.681643Z

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Pith citing papers

Observation e0931b2e-16dc-4905-88f4-19bbe39d7ac7 · inbound

"Noisier" Noise Contrastive Eestimation is (Almost) Maximum Likelihood cites this paper.

"Noisier" Noise Contrastive Eestimation is (Almost) Maximum Likelihood The GAN is dead; long live the GAN! A Modern GAN Baseline

Reference 5

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Observation 7bea3531-4d91-4bc5-8a5c-a41d2b65fbde · inbound

Recovering Sub-threshold S-wave Arrivals in Deep Learning Phase Pickers via Shape-Aware Loss cites this paper.

Recovering Sub-threshold S-wave Arrivals in Deep Learning Phase Pickers via Shape-Aware Loss The GAN is dead; long live the GAN! A Modern GAN Baseline

Reference 10

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Observation 7308c216-8f56-4c5a-bf1c-e260093ea199 · inbound

Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling cites this paper.

Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling The GAN is dead; long live the GAN! A Modern GAN Baseline

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T10:16:28.948203Z

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Observation 62c2ac77-1734-4c0c-985d-cd42100fbfc4 · inbound

Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data cites this paper.

Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data The GAN is dead; long live the GAN! A Modern GAN Baseline

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:57:21.577715Z

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