Paper Citation Record · LEDGER

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients

As of 23 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2412.07565.

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

pith.paper-citation-record.v1
2412.07565 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T18:45:38.124392Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

verified exact0
verified fuzzy16
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ee5f0247-65fe-4794-aaa9-310d9ed6b61e · outbound

This paper cites A survey of uncertainty in deep neural networks,.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients A survey of uncertainty in deep neural networks,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:45:38.726788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0704904a-3628-4a39-b7b8-67d1a61e0c8a · outbound

This paper cites A tragic loss,.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients A tragic loss,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:45:38.707226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2064bf38-9eef-452b-a3d6-682f2ac64d4f · outbound

This paper cites Out-of- distribution detection for adaptive computer vision,.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients Out-of- distribution detection for adaptive computer vision,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:45:38.690954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3a7f3266-b09e-4852-a974-fb7afe455225 · outbound

This paper cites YOLOv4: Optimal Speed and Accuracy of Object Detection.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients YOLOv4: Optimal Speed and Accuracy of Object Detection

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T18:45:37.982001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba61d377-00ae-4e18-b6b6-6c20f8755fc6 · outbound

This paper cites Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T18:45:37.988366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68f2a591-9c0b-4d93-9462-33f428fe1630 · outbound

This paper cites Image anomaly detection with generative adversarial networks,.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients Image anomaly detection with generative adversarial networks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:45:38.672622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4d522bfa-9adf-4ae8-b0ac-71678c04c6f2 · outbound

This paper cites Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data,.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:45:38.653856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e0a31f85-bde9-4d04-acae-8c679fd0cc8c · outbound

This paper cites Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T18:45:38.006622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98aa98cd-3091-4c99-80f6-e4ccb64305c5 · outbound

This paper cites Contrastive Training for Improved Out-of-Distribution Detection.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients Contrastive Training for Improved Out-of-Distribution Detection

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T18:45:38.012084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4af5b424-108d-46b8-b527-6de62cedd61f · outbound

This paper cites Likelihood ratios for out-of-distribution detection,.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients Likelihood ratios for out-of-distribution detection,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:45:38.633833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f5087855-c215-4b3a-87b3-f5d9b1a004a3 · outbound

This paper cites Self-supervised learning for generalizable out-of-distribution detection,.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients Self-supervised learning for generalizable out-of-distribution detection,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:45:38.614638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 12d5fa25-e13d-46da-a87e-c6c6682d9761 · outbound

This paper cites Embedded out-of-distribution detection on an autonomous robot platform,.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients Embedded out-of-distribution detection on an autonomous robot platform,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:45:38.596244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation da2ec47d-4d06-42af-9181-22d3e7b2e158 · outbound

This paper cites Robustness to out-of-distribution inputs via task-aware generative uncertainty,.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients Robustness to out-of-distribution inputs via task-aware generative uncertainty,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:45:38.578507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8f2a0d76-5201-4e4f-b16e-cb3ccd5d93ff · outbound

This paper cites An anomaly detection method using deep convolution neural network for vision image of robot,.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients An anomaly detection method using deep convolution neural network for vision image of robot,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:45:38.558430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c1ed17f0-b82a-43e7-8007-86a755c600e1 · outbound

This paper cites Unsupervised anomaly detection for a smart autonomous robotic assistant surgeon (saras) using a deep residual autoencoder,.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients Unsupervised anomaly detection for a smart autonomous robotic assistant surgeon (saras) using a deep residual autoencoder,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:45:38.537775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 26a1d0d7-8abc-4a26-bb78-5fd6ef24e1d0 · outbound

This paper cites Self-calibrating anomaly and change detection for autonomous inspection robots,.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients Self-calibrating anomaly and change detection for autonomous inspection robots,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:45:38.515798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 539eaf23-8382-42d4-b1de-05ac90d569f1 · outbound

This paper cites Why Normalizing Flows Fail to Detect Out-of-Distribution Data,.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients Why Normalizing Flows Fail to Detect Out-of-Distribution Data,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:45:38.495135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 798e3602-2f0c-42a4-9665-4085b3bf0df3 · outbound

This paper cites Normalizing Flows for Probabilistic Modeling and Inference,.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients Normalizing Flows for Probabilistic Modeling and Inference,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:45:38.477156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e2fe1b60-5623-403b-b41c-245becdf7534 · outbound

This paper cites Generalized Out-of-Distribution Detection: A Survey.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients Generalized Out-of-Distribution Detection: A Survey

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T18:45:38.067405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2453683e-af21-42c5-baf6-ccb4bb2ea111 · outbound

This paper cites Density estimation using Real NVP.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients Density estimation using Real NVP

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T18:45:38.073284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02aa010f-c74d-41b6-8c26-16ee42c22d41 · outbound

This paper cites Glow: Generative Flow with Invertible 1x1 Convolutions.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients Glow: Generative Flow with Invertible 1x1 Convolutions

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T18:45:38.078964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8237c64-bbaa-4f44-8f2e-40f0e6f949d3 · outbound

This paper cites Audet and W.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients Audet and W

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:45:38.457949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation af915a4a-1a32-40fb-8694-43d053366fee · outbound

This paper cites Adam: A method for stochastic optimization,.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients Adam: A method for stochastic optimization,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:45:38.440005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 71d1a77b-1d66-4653-ad75-523cf6e50547 · outbound

This paper cites EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T18:45:38.094351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84244962-bad5-4f64-a4ff-707a90ed8fc2 · outbound

This paper cites ImageNet Large Scale Visual Recognition Challenge,.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients ImageNet Large Scale Visual Recognition Challenge,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T18:45:38.101118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a3ccebb-c294-4ef5-b54d-d17907e1b50d · outbound

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

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients Learning multiple layers of features from tiny images,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T18:45:38.106513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b27cc68a-022d-4cd0-b71e-4775b7ea6e6a · outbound

This paper cites Reading digits in natural images with unsupervised feature learning,.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients Reading digits in natural images with unsupervised feature learning,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T18:45:38.113422Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b95e291-8d29-436c-80a7-45a4fb73c8f0 · outbound

This paper cites Deep learning face attributes in the wild,.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients Deep learning face attributes in the wild,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T18:45:38.118652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf326246-db61-433a-b6b9-8e03f6d7f722 · outbound

This paper cites Microsoft COCO: Common Objects in Context.

Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients Microsoft COCO: Common Objects in Context

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T18:45:38.124392Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.