Paper Citation Record · LEDGER

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning

As of 10 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 1 inbound Pith citation observation for arXiv:2506.00136.

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

pith.paper-citation-record.v1
2506.00136 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:15:29.592118Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-12T01:24:17.156028Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

50 of 50 outbound references displayed

verified exact3
verified fuzzy27
unresolved19
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 24653cc9-7a3d-4ced-a7ed-fb7c6772951e · outbound

This paper cites Vetrov, and Christian Andersson Naesseth.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Vetrov, and Christian Andersson Naesseth

Reference 1

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Observation 18f8070f-1f99-4a59-b36f-019f150956cf · outbound

This paper cites MINE: Mutual Information Neural Estimation.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning MINE: Mutual Information Neural Estimation

Reference 2

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Observation 23d62f9f-e72c-4565-bfaa-df4a1b39cb26 · outbound

This paper cites Representation Learning: A Review and New Perspectives.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Representation Learning: A Review and New Perspectives

Reference 3

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Observation 8b93d918-6e37-473b-82f7-47db98fddfc8 · outbound

This paper cites Pixelsnail: An improved autoregressive generative model.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Pixelsnail: An improved autoregressive generative model

Reference 4

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Observation caf0a3a6-e2d5-4014-8776-626e4ac2d476 · outbound

This paper cites Diffusion models beat gans on image syn- thesis.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Diffusion models beat gans on image syn- thesis

Reference 5

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Observation a47efc30-2422-44b0-b370-d733bfe02796 · outbound

This paper cites Denoising diffusion probabilistic models.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Denoising diffusion probabilistic models

Reference 6

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Observation 95ca844c-ad1c-4b57-b384-1a4b640d0ab7 · outbound

This paper cites Hudson, Daniel Zoran, Mateusz Malinowski, Andrew K.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Hudson, Daniel Zoran, Mateusz Malinowski, Andrew K

Reference 7

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Observation 72160000-33a0-4aa0-bdd8-89949c0425f4 · outbound

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On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Unresolved cited work

Reference 8

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Observation 8a8db6b9-71b7-4518-929e-809d51944c39 · outbound

This paper cites Auto-Encoding Variational Bayes.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Auto-Encoding Variational Bayes

Reference 9

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Observation 6a993358-700d-4809-8004-6729aeb37b84 · outbound

This paper cites Kingma, Tim Salimans, Ben Poole, and Jonathan Ho.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Kingma, Tim Salimans, Ben Poole, and Jonathan Ho

Reference 10

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Observation 4cba30d0-116a-4847-ad90-f47c55b11b34 · outbound

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

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Learning multiple layers of features from tiny images

Reference 11

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Observation 8b9aa703-b447-4af5-b03b-c2f5cd48aa74 · outbound

This paper cites Hierarchical VAE with a Diffusion-based VampPrior.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Hierarchical VAE with a Diffusion-based VampPrior

Reference 12

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Observation 52e1d58c-6f48-429c-ab6b-dbc7d621d7a1 · outbound

This paper cites Priorgrad: Improving conditional denoising diffusion models with data-dependent adaptive prior.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Priorgrad: Improving conditional denoising diffusion models with data-dependent adaptive prior

Reference 13

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Observation c2084f6b-9b55-4f0f-a2f3-b280b0956327 · outbound

This paper cites I$^2$SB: Image-to-Image Schr\"odinger Bridge.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning I$^2$SB: Image-to-Image Schr\"odinger Bridge

Reference 14

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Observation 15739325-da92-4711-90f3-40096930a6ed · outbound

This paper cites Flow straight and fast: Learning to generate and transfer data with rectified flow.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Flow straight and fast: Learning to generate and transfer data with rectified flow

Reference 15

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Observation 35757f5e-0ace-4b47-9975-96fa7d8fdbb3 · outbound

This paper cites Deep learning face attributes in the wild.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Deep learning face attributes in the wild

Reference 16

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Observation aa9d0639-1f01-498c-ae68-e8228f39ac6c · outbound

This paper cites Decoupled weight decay regularization.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Decoupled weight decay regularization

Reference 17

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Observation 1599c538-3e42-4a96-afa8-6c99edd747f6 · outbound

This paper cites Sdedit: Guided image synthesis and editing with stochastic differential equations.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Sdedit: Guided image synthesis and editing with stochastic differential equations

Reference 18

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Source-reported events for the cited work

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Observation 252582ec-a70e-4c1a-9c0b-b3745d231b1e · outbound

This paper cites Discrete Sequential Prediction of Continuous Actions for Deep RL.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Discrete Sequential Prediction of Continuous Actions for Deep RL

Reference 19

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Observation 185ce075-ee6f-4a18-a1ee-be89358a27fb · outbound

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On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Unresolved cited work

Reference 20

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Observation 7759a2d9-dd1d-4b4f-964b-7ed0abfd7356 · outbound

This paper cites Diamos, Erich Elsen, David García, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, and Hao Wu.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Diamos, Erich Elsen, David García, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, and Hao Wu

Reference 21

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Observation e7756d94-98bd-4648-9bad-b3b85e9d9b5f · outbound

This paper cites Improved denoising diffusion probabilistic models.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Improved denoising diffusion probabilistic models

Reference 22

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Observation b3383197-b190-42ab-a6b1-499de91a4285 · outbound

This paper cites Diffenc: Variational diffusion with a learned encoder.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Diffenc: Variational diffusion with a learned encoder

Reference 23

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Observation ea9e6afd-0a20-4756-8bce-f154c1ddd719 · outbound

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On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Input perturbation reduces exposure bias in diffusion models

Reference 24

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Observation b6a0034f-60e1-4462-b1ec-fa4c11464aab · outbound

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On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Unresolved cited work

Reference 25

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Observation 7f5aba87-e353-4e27-8b04-3aa56c29a17c · outbound

This paper cites DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents

Reference 26

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This paper cites Pytorch: An imperative style, high-performance deep learning library.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Pytorch: An imperative style, high-performance deep learning library

Reference 27

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Observation 35fbae26-b7e3-4f98-99a9-fea72015ed66 · outbound

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

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Diffusion autoencoders: Toward a meaningful and decodable representation

Reference 28

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This paper cites Generating diverse high-fidelity images with VQ-V AE-2.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Generating diverse high-fidelity images with VQ-V AE-2

Reference 29

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This paper cites Discrete variational autoencoders.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Discrete variational autoencoders

Reference 30

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On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning pytorch-fid: FID Score for PyTorch

Reference 31

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This paper cites Denoising diffusion implicit models.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Denoising diffusion implicit models

Reference 32

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This paper cites Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole

Reference 33

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Observation b098b91a-5218-4d0c-8591-869160cc4023 · outbound

This paper cites Dual diffusion implicit bridges for image-to-image translation.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Dual diffusion implicit bridges for image-to-image translation

Reference 34

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Observation 688790ce-fb0a-4907-9518-cb88b42d65a0 · outbound

This paper cites NV AE: A deep hierarchical variational autoencoder.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning NV AE: A deep hierarchical variational autoencoder

Reference 35

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This paper cites Macready, Zhengbing Bian, Amir Khoshaman, and Evgeny Andriyash.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Macready, Zhengbing Bian, Amir Khoshaman, and Evgeny Andriyash

Reference 36

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On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Neural discrete representation learning

Reference 37

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Observation 4f3348b9-b078-4327-96f8-431c153cdb08 · outbound

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

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Infodiffusion: Representation learning using information maximizing diffusion models

Reference 38

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Observation 6f3af21e-0358-44ef-9d79-cb3148e0e07b · outbound

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On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Binary latent diffusion

Reference 39

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On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Disdiff: Unsupervised disentanglement of diffusion probabilistic models

Reference 41

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Observation 010eb39c-ebb0-4dc5-a011-2d5ea112e933 · outbound

This paper cites Diffusion model as representation learner.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Diffusion model as representation learner

Reference 42

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Observation b8fdcd97-6542-4180-b9cd-c78a279efe5b · outbound

This paper cites Exploring diffusion time-steps for unsupervised representation learning.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Exploring diffusion time-steps for unsupervised representation learning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:15:30.795580Z

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Observation caa0228a-a387-4e01-babd-3f5e8ca402c8 · outbound

This paper cites Efros, Eli Shechtman, and Oliver Wang.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Efros, Eli Shechtman, and Oliver Wang

Reference 44

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Observation 11bb0f8d-a602-4863-a057-bfe44e8ec68c · outbound

This paper cites Unsupervised representation learning from pre-trained diffusion probabilistic models.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Unsupervised representation learning from pre-trained diffusion probabilistic models

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:15:30.663493Z

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Observation 798048a3-727e-4f86-a0a7-d9818d9ff850 · outbound

This paper cites Unsupervised Discovery of Interpretable Directions in h-space of Pre-trained Diffusion Models.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Unsupervised Discovery of Interpretable Directions in h-space of Pre-trained Diffusion Models

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:15:29.797908Z

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Observation 63678ac0-dbed-4bde-b9cb-294e0e0ef6f0 · outbound

This paper cites Shiftddpms: Exploring conditional diffusion models by shifting diffusion trajectories.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Shiftddpms: Exploring conditional diffusion models by shifting diffusion trajectories

Reference 47

Resolution: verified exact; doi, observed 2026-08-07T12:15:30.560614Z

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Observation 53f1e01e-000c-478b-b769-60485a2c9204 · outbound

This paper cites InfoVAE: Information Maximizing Variational Autoencoders.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning InfoVAE: Information Maximizing Variational Autoencoders

Reference 48

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Observation 254de4e0-aed9-4cdc-90be-c14032053e7d · outbound

This paper cites Denoising diffusion bridge models.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Denoising diffusion bridge models

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:15:30.438433Z

Source-reported events for the cited work

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Observation 55d2cfb5-8192-4bfc-a63a-e5323178f232 · outbound

This paper cites Discrete Autoencoders for Sequence Models.

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Discrete Autoencoders for Sequence Models

Reference 50

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Observation d635b594-4b72-486f-8d1e-97e9ba49bcae · outbound

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On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning Variational Diffusion Models

Reference 2021

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Pith citing papers

Observation 9759b947-03cd-455c-8a76-25e0cb2c811e · inbound

When Geometry Aligns: Dihedral Hidden-State Transformations in UNet, ViT, and DiT Architectures cites this paper.

When Geometry Aligns: Dihedral Hidden-State Transformations in UNet, ViT, and DiT Architectures On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning

Reference 6

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Source-reported events for the cited work

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