Paper Citation Record · LEDGER

Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 inbound Pith citation observations for arXiv:2011.10650.

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

pith.paper-citation-record.v1
2011.10650 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 19 of 19 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:20:16.161693Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T11:37:03.454175Z

Reference resolution

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation ecd11f75-bdf9-4c02-9e14-5545a7a7805d · inbound

Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed cites this paper.

Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-17T03:56:19.965934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b0971977-3e4b-4ff5-9629-75125f67b8e2 · inbound

Improved Denoising Diffusion Probabilistic Models cites this paper.

Improved Denoising Diffusion Probabilistic Models Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-16T19:19:14.968002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ea4736a9-b6b0-42c0-9e15-35fc45d4bb7b · inbound

VideoGPT: Video Generation using VQ-VAE and Transformers cites this paper.

VideoGPT: Video Generation using VQ-VAE and Transformers Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-13T17:24:33.764779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 71695042-96ef-44f5-b4d7-c75741fbc857 · inbound

Diffusion Models Beat GANs on Image Synthesis cites this paper.

Diffusion Models Beat GANs on Image Synthesis Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-13T11:16:28.526079Z

Source-reported events for the cited work

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Observation f05ce6bd-d819-47a1-b9d3-63c505e9e955 · inbound

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

High-Resolution Image Synthesis with Latent Diffusion Models Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-11T22:02:10.232350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a72d135d-d965-4b68-a6cb-a91bf7d38164 · inbound

Hierarchical Text-Conditional Image Generation with CLIP Latents cites this paper.

Hierarchical Text-Conditional Image Generation with CLIP Latents Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-10T16:55:57.671541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9bd7e3ce-4bfd-42af-838a-9dd6d3a09d66 · inbound

Mastering Diverse Domains through World Models cites this paper.

Mastering Diverse Domains through World Models Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:08:22.188795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b7d77c59-f849-42b0-897e-90c5731dda8b · inbound

DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectory cites this paper.

DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectory Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

Reference 150

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:03:58.215322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bd5c4be5-f0c5-43eb-90f6-ace2e9382977 · inbound

Improving Brain-to-Image Reconstruction via Fine-Grained Text Bridging cites this paper.

Improving Brain-to-Image Reconstruction via Fine-Grained Text Bridging Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T13:20:16.161693Z

Source-reported events for the cited work

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Observation 33d0c59e-d594-4749-b01f-0b9d4140a5da · inbound

TaxaDiffusion: Progressively Trained Diffusion Model for Fine-Grained Species Generation cites this paper.

TaxaDiffusion: Progressively Trained Diffusion Model for Fine-Grained Species Generation Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:52.307324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 790e82d5-096c-4a77-a0d5-4c3deddf8020 · inbound

Diffusion Counterfactual Generation with Semantic Abduction cites this paper.

Diffusion Counterfactual Generation with Semantic Abduction Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T05:30:32.855134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dad4e1d9-89d1-4636-b8e9-6a6c3911c8e5 · inbound

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation cites this paper.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:16.852350Z

Source-reported events for the cited work

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Observation e1960aaf-e50d-4481-9635-ba524d44ce1a · inbound

Tractable Representation Learning with Probabilistic Circuits cites this paper.

Tractable Representation Learning with Probabilistic Circuits Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-06T19:54:21.024377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c658fee2-6be6-406d-8842-8fa272ae6c53 · inbound

Probabilistic cosmological inference on HI tomographic data cites this paper.

Probabilistic cosmological inference on HI tomographic data Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T12:36:37.807470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79852f20-8b45-4a18-8b42-2b47b4d09f1e · inbound

VAMP-Diff: VampPrior Latent Diffusion for Photoplethysmography Modeling cites this paper.

VAMP-Diff: VampPrior Latent Diffusion for Photoplethysmography Modeling Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-25T00:20:08.089932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9fbe870e-bccc-4e87-99f6-fe0bd942d6e7 · inbound

Multigrid Training for Molecular Generation using Graph Neural Networks cites this paper.

Multigrid Training for Molecular Generation using Graph Neural Networks Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

Reference 4

Resolution: malformed identifier; arxiv_id, observed 2026-07-04T08:39:42.206649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6d4e5874-34bb-4ec5-97e1-56968d08f46b · inbound

Language-Assisted Super-Resolution from Real-World Low-Resolution Patches cites this paper.

Language-Assisted Super-Resolution from Real-World Low-Resolution Patches Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

Reference 84

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T10:05:41.472618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ec34a1a8-5a82-415e-97d9-5bc098e58b7c · inbound

Language-Assisted Super-Resolution from Real-World Low-Resolution Patches cites this paper.

Language-Assisted Super-Resolution from Real-World Low-Resolution Patches Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

Reference 84

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T22:18:59.552173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ec0cf0f9-dd19-4d63-b872-f512a9cdc163 · inbound

Unpaired Joint Distribution Modeling via Multi-Scale Image Representations cites this paper.

Unpaired Joint Distribution Modeling via Multi-Scale Image Representations Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-07-10T11:37:03.456436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.