Paper Citation Record · LEDGER

Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:1906.02994.

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

pith.paper-citation-record.v1
1906.02994 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 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 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:43:14.284760Z

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

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

59
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 219a314f-d6f7-47b8-8756-dbb850c5a748 · inbound

OOD Detection with immature Models cites this paper.

OOD Detection with immature Models Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-09T17:43:14.284760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfa2e993-eeb0-4354-b48b-cb8669a125a6 · inbound

Safeguarding AI in Medical Imaging: Post-Hoc Out-of-Distribution Detection with Normalizing Flows cites this paper.

Safeguarding AI in Medical Imaging: Post-Hoc Out-of-Distribution Detection with Normalizing Flows Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-23T03:12:27.739141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation afa89e05-3e14-4b60-991b-01d76a092d54 · inbound

Zero-Shot Image Anomaly Detection Using Generative Foundation Models cites this paper.

Zero-Shot Image Anomaly Detection Using Generative Foundation Models Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T11:31:32.162467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffcee040-4978-48ea-afef-6a8e605a6a64 · inbound

Towards accurate extreme event likelihoods from diffusion model climate emulators cites this paper.

Towards accurate extreme event likelihoods from diffusion model climate emulators Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:11:25.923614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f54c2619-8707-4fa0-8403-aeb0413b7e75 · inbound

Scaling Pretrained Representations Enables Label-Free Out-of-Distribution Detection Without Fine-Tuning cites this paper.

Scaling Pretrained Representations Enables Label-Free Out-of-Distribution Detection Without Fine-Tuning Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality

Reference 36

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T18:36:09.039476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cc82f7c3-6adf-43ff-b1ab-9d8193fc3624 · inbound

Beyond Penalization: Diffusion-based Out-of-Distribution Detection and Selective Regularization in Offline Reinforcement Learning cites this paper.

Beyond Penalization: Diffusion-based Out-of-Distribution Detection and Selective Regularization in Offline Reinforcement Learning Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality

Reference 34

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T07:41:49.951293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 33b75133-ea19-40f3-8a7a-c9749797810b · inbound

Privacy Policy Enforcement Guardrails for Data-Sensitive Retrieval-Augmented Generation cites this paper.

Privacy Policy Enforcement Guardrails for Data-Sensitive Retrieval-Augmented Generation Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-19T20:27:48.760831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9c4fbb18-0439-45b8-b628-77c60f5c38e9 · inbound

Privacy Policy Enforcement Guardrails for Data-Sensitive Retrieval-Augmented Generation cites this paper.

Privacy Policy Enforcement Guardrails for Data-Sensitive Retrieval-Augmented Generation Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-06-30T19:15:00.923476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 66bd8b3e-92ce-4288-b810-64a67d913df8 · inbound

The Signal in the Noise: OOD Detection Through Goodness-of-Fit Testing in Factorised Latent Spaces cites this paper.

The Signal in the Noise: OOD Detection Through Goodness-of-Fit Testing in Factorised Latent Spaces Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-22T08:11:17.155658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 04cd63ea-bcf9-4a6e-8c3f-3152e5ef2d0f · inbound

KLIP: localized distribution shift detection via KL-divergence with diffusion priors in Inverse Problems cites this paper.

KLIP: localized distribution shift detection via KL-divergence with diffusion priors in Inverse Problems Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-07-01T19:15:59.983383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d2d659db-b461-41f4-bfc7-ac52c1fa9773 · inbound

StressDream: Steering Video World Models for Robust Policy Evaluation and Improvement cites this paper.

StressDream: Steering Video World Models for Robust Policy Evaluation and Improvement Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-07-01T19:25:59.791614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f73c696f-c752-4d38-aa85-5f472258a41c · inbound

Hacking Generative Perplexity: Why Unconditional Text Evaluation Needs Distributional Metrics cites this paper.

Hacking Generative Perplexity: Why Unconditional Text Evaluation Needs Distributional Metrics Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T22:27:26.070225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ba13119f-876b-48c1-a8c8-154fef121175 · inbound

Hacking Generative Perplexity: Why Unconditional Text Evaluation Needs Distributional Metrics cites this paper.

Hacking Generative Perplexity: Why Unconditional Text Evaluation Needs Distributional Metrics Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T04:51:23.471209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.