Paper Citation Record · LEDGER

Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2103.04922.

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

pith.paper-citation-record.v1
2103.04922 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:46:51.775224Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T13:03:58.207640Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation ab64e0ef-609a-498f-bc9e-50db8a6dd7ae · 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 Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models

Reference 100

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:03:58.209406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 77036ac3-c00b-4de9-a4d4-e93da6eec5c6 · inbound

Comprehensive Review of EEG-to-Output Research: Decoding Neural Signals into Images, Videos, and Audio cites this paper.

Comprehensive Review of EEG-to-Output Research: Decoding Neural Signals into Images, Videos, and Audio Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-10T23:46:51.775224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39c5c5b8-7132-4219-b876-1d0f2a5c61a2 · inbound

Adversarial Semantic Augmentation for Training Generative Adversarial Networks under Limited Data cites this paper.

Adversarial Semantic Augmentation for Training Generative Adversarial Networks under Limited Data Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-09T17:44:26.065739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7667107-aabf-49d7-960a-8a5203627c64 · inbound

Energy-based models for diagnostic reconstruction and analysis in a laboratory plasma device cites this paper.

Energy-based models for diagnostic reconstruction and analysis in a laboratory plasma device Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:21:25.933697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.