Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation | Paper Citation Record

Observation 6d926b14-0310-44ac-9560-323e660696d6 · outbound

This paper cites Learning transferable visual models from natural lan- guage supervision.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Learning transferable visual models from natural lan- guage supervision

Reference 1

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Observation ba0eb5e3-fbb7-4318-b54d-60ae8e7fb677 · outbound

This paper cites MaskCLIP: Masked self-distillation advances contrastive language-image pretraining.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation MaskCLIP: Masked self-distillation advances contrastive language-image pretraining

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:52.539923Z

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Observation 727ed13a-d712-4dda-8d52-31bb9a723b3c · outbound

This paper cites Brandt, Axel Feldmann, Zhoutong Zhang, and William T.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Brandt, Axel Feldmann, Zhoutong Zhang, and William T

Reference 3

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Observation 83277068-a675-4078-827f-48814603f42f · outbound

This paper cites Segment Anything.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Segment Anything

Reference 4

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Observation 8febfad2-dc87-4e1c-8d43-c3df3e590ac7 · outbound

This paper cites Ambler: An autonomous rover for planetary exploration.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Ambler: An autonomous rover for planetary exploration

Reference 5

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Observation 1fd53763-1640-463c-a3f9-096d9f113700 · outbound

This paper cites Autonomous navigation system for plan- etary exploration rover based on artificial potential fields.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Autonomous navigation system for plan- etary exploration rover based on artificial potential fields

Reference 6

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Observation 35cdccbb-0e2a-42ab-b1d8-b4b4a1856d8c · outbound

This paper cites Locomotion policy guided traversability learning using volumetric representations of complex environ- ments.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Locomotion policy guided traversability learning using volumetric representations of complex environ- ments

Reference 7

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Observation 08ddd460-cac9-4624-b816-9263ca868e87 · outbound

This paper cites TerrainNet: Visual Modeling of Complex Terrain for High-speed, Off-road Navigation.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation TerrainNet: Visual Modeling of Complex Terrain for High-speed, Off-road Navigation

Reference 8

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Observation 42d2948e-5e65-4217-932f-d1d13422d958 · outbound

This paper cites Semantic terrain classification for off-road autonomous driving.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Semantic terrain classification for off-road autonomous driving

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:50.886363Z

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Observation efef0575-2629-4d86-ae48-a90fd2b110e9 · outbound

This paper cites Visually augmented navigation for autonomous under- water vehicles.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Visually augmented navigation for autonomous under- water vehicles

Reference 10

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Observation 76f78e9f-4337-4744-a301-eb08bfc5b470 · outbound

This paper cites Vision-Based Goal- Conditioned Policies for Underwater Navigation in the Presence of Obstacles.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Vision-Based Goal- Conditioned Policies for Underwater Navigation in the Presence of Obstacles

Reference 11

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Observation 6093ad30-aa5a-4cc9-bc88-46e565fb89c8 · outbound

This paper cites Deep multispectral semantic scene understanding of forested environments using multimodal fusion.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Deep multispectral semantic scene understanding of forested environments using multimodal fusion

Reference 12

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Observation 8d8e714b-2256-4a12-81c0-0001c66fc9c2 · outbound

This paper cites AdapNet: Adaptive semantic segmenta- tion in adverse environmental conditions.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation AdapNet: Adaptive semantic segmenta- tion in adverse environmental conditions

Reference 13

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Observation 156ca094-a312-4d28-9413-d7ecb9a7624d · outbound

This paper cites GA-Nav: Efficient terrain segmen- tation for robot navigation in unstructured outdoor environments.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation GA-Nav: Efficient terrain segmen- tation for robot navigation in unstructured outdoor environments

Reference 14

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Observation 14f62934-e8bb-4797-9607-54dd821f1bd0 · outbound

This paper cites A RUGD dataset for autonomous navigation and visual perception in un- structured outdoor environments.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation A RUGD dataset for autonomous navigation and visual perception in un- structured outdoor environments

Reference 15

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Observation 18231d89-c8da-4cb7-b043-4e04efe200b9 · outbound

This paper cites RELLIS-3D dataset: Data, bench- marks and analysis.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation RELLIS-3D dataset: Data, bench- marks and analysis

Reference 16

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This paper cites Osteen, and Nicholas Roy.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Osteen, and Nicholas Roy

Reference 17

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Observation ab9b5021-c7e7-474a-985e-07fa95f4bb7e · outbound

This paper cites Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation

Reference 18

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Observation 983ca307-dd6e-49ec-bafd-83b3c7980d64 · outbound

This paper cites Posterior network: Uncertainty estimation without OOD samples via density-based pseudo-counts.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Posterior network: Uncertainty estimation without OOD samples via density-based pseudo-counts

Reference 19

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This paper cites Natural posterior network: Deep Bayesian predictive uncertainty for exponential family distributions.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Natural posterior network: Deep Bayesian predictive uncertainty for exponential family distributions

Reference 20

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Observation ed6850c2-c747-436c-9e17-b51de1f57ef9 · outbound

This paper cites Dense out-of-distribution detection by robust learning on synthetic negative data.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Dense out-of-distribution detection by robust learning on synthetic negative data

Reference 21

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Observation a854523c-4320-4392-858b-28622ec5036e · outbound

This paper cites Residual pattern learning for pixel-wise out- of-distribution detection in semantic segmentation.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Residual pattern learning for pixel-wise out- of-distribution detection in semantic segmentation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:49.252105Z

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Observation 133e6da4-4051-4581-8315-802cbb25e4eb · outbound

This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Reference 23

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This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Simple and scalable predictive uncertainty estimation using deep ensembles

Reference 24

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Observation 4b189888-fc58-42cf-88f2-5c2b14b9ea93 · outbound

This paper cites A simple unified framework for detecting out-of- distribution samples and adversarial attacks.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation A simple unified framework for detecting out-of- distribution samples and adversarial attacks

Reference 25

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Observation 6ac6189c-2dc0-4f75-9349-e63f4733c8b0 · outbound

This paper cites Concurrent misclassification and out-of-distribution de- tection for semantic segmentation via energy-based nor- malizing flow.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Concurrent misclassification and out-of-distribution de- tection for semantic segmentation via energy-based nor- malizing flow

Reference 26

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This paper cites Pixel-wise energy-biased abstention learning for anomaly segmenta- tion on complex urban driving scenes.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Pixel-wise energy-biased abstention learning for anomaly segmenta- tion on complex urban driving scenes

Reference 27

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Observation acc58521-7fa0-4648-8cb7-26a60149a0e9 · outbound

This paper cites Dense open-set recognition based on training with noisy negative images.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Dense open-set recognition based on training with noisy negative images

Reference 28

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This paper cites Entropy maximization and meta classification for out- of-distribution detection in semantic segmentation.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Entropy maximization and meta classification for out- of-distribution detection in semantic segmentation

Reference 29

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This paper cites RbA: Segmenting unknown regions rejected by all.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation RbA: Segmenting unknown regions rejected by all

Reference 30

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Observation c89b5835-3e2b-4584-b399-76907a0527e6 · outbound

This paper cites Maskomaly:Zero-Shot Mask Anomaly Segmentation.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Maskomaly:Zero-Shot Mask Anomaly Segmentation

Reference 31

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Observation d6c37703-4595-40fe-bc0f-a3b4d85f14b2 · outbound

This paper cites Denoising diffusion probabilistic models.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Denoising diffusion probabilistic models

Reference 32

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Observation 7ef2daa6-3ca0-4518-b869-33193151efa7 · outbound

This paper cites Diffusion models in vision: A survey.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Diffusion models in vision: A survey

Reference 33

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Observation 320d869e-85f6-4943-a933-8b49293fbd0f · outbound

This paper cites Diffusion models beat GANs on image synthesis.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Diffusion models beat GANs on image synthesis

Reference 34

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Observation bb14e7f0-901e-470c-9762-2625eef6e489 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation High-resolution image synthesis with latent diffusion models

Reference 35

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This paper cites Analysis by synthesis: a (re-) emerging program of research for language and vision.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Analysis by synthesis: a (re-) emerging program of research for language and vision

Reference 36

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This paper cites Vision as Bayesian inference: analysis by synthesis? Trends in cognitive sciences, 10(7):301–308, 2006.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Vision as Bayesian inference: analysis by synthesis? Trends in cognitive sciences, 10(7):301–308, 2006

Reference 37

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This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Deep unsupervised learning using nonequilibrium thermodynamics

Reference 38

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

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole

Reference 39

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This paper cites Reduce, reuse, recycle: Compositional generation with energy-based diffusion models and MCMC.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Reduce, reuse, recycle: Compositional generation with energy-based diffusion models and MCMC

Reference 40

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This paper cites DALL-E: Zero-shot text-to-image generation.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation DALL-E: Zero-shot text-to-image generation

Reference 41

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This paper cites Dif- fusion policy: Visuomotor policy learning via action diffusion.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Dif- fusion policy: Visuomotor policy learning via action diffusion

Reference 42

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This paper cites Octo: An open-source generalist robot policy.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Octo: An open-source generalist robot policy

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:46.011602Z

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Observation 0f613a25-bdea-4232-9fe8-8a98190bcdf5 · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Image quality assessment: from error visibility to structural similarity

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:45.828703Z

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Observation 7404f922-53ca-4ecf-b890-2b6c9cb1e067 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation DINOv2: Learning Robust Visual Features without Supervision

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:45.629754Z

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This paper cites Deep residual learning for image recognition.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Deep residual learning for image recognition

Reference 46

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Observation 45d984fc-7e4b-4f73-bcb6-6ac8f13aea07 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 47

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This paper cites Emerging Properties in Self-Supervised Vision Transformers.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Emerging Properties in Self-Supervised Vision Transformers

Reference 48

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This paper cites Segment- MeIfYouCan: A benchmark for anomaly segmentation.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Segment- MeIfYouCan: A benchmark for anomaly segmentation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:44.931045Z

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Observation ac137fd1-ec6f-43f9-8d9f-d56025d39646 · outbound

This paper cites Normalizing flows for probabilistic modeling and inference.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Normalizing flows for probabilistic modeling and inference

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:44.748672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 45aef190-2665-4186-b9e3-f413fe17ae4a · outbound

This paper cites Denoising diffusion implicit models.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Denoising diffusion implicit models

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T13:06:37.183981Z

Source-reported events for the cited work

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Observation de04dad0-dc79-49e6-a0e4-ca7af4aa06ba · outbound

This paper cites Deep learning for anomaly detection: A review.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Deep learning for anomaly detection: A review

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:44.558621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1e1d3467-1833-49e2-8ca0-0aee2e2719b4 · outbound

This paper cites Robust principal component analysis? Journal of the ACM (JACM) , 58(3):1–37, 2011.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Robust principal component analysis? Journal of the ACM (JACM) , 58(3):1–37, 2011

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:44.388665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6c6ff476-2ebd-42bd-bb7a-3329bd0f33eb · outbound

This paper cites Kernel principal component analysis.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Kernel principal component analysis

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:44.171362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation acb65970-3d24-4985-9c9b-c79270ec5680 · outbound

This paper cites Anomaly detection with robust deep autoencoders.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Anomaly detection with robust deep autoencoders

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:43.983443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 892cda6b-b582-4b4b-96bf-818d65cd5893 · outbound

This paper cites Very sparse random projections.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Very sparse random projections

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:43.804451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9ac42a5e-4f09-47d7-bf27-bc3e36ace2c0 · outbound

This paper cites Learning representations of ultrahigh-dimensional data for random distance-based outlier detection.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Learning representations of ultrahigh-dimensional data for random distance-based outlier detection

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:43.644055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c421800f-30ba-4388-a58b-89865787b8b4 · outbound

This paper cites Loda: Lightweight on-line detector of anomalies.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Loda: Lightweight on-line detector of anomalies

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:43.443024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f93883dc-9ce5-476a-ab61-944d881c8645 · outbound

This paper cites A review of novelty detection.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation A review of novelty detection

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:43.208402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 903de6e9-854b-49df-a654-f588f0cbba55 · outbound

This paper cites Autonomous navigation in unknown environments using machine learning.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Autonomous navigation in unknown environments using machine learning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:43.041554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 329be224-e69b-418d-9ea4-a972c84752d6 · outbound

This paper cites High- dimensional and large-scale anomaly detection using a linear one-class svm with deep learning.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation High- dimensional and large-scale anomaly detection using a linear one-class svm with deep learning

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:42.866429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b3d767d5-4e04-49c6-aa85-6f56b78ec49c · outbound

This paper cites Object-centric auto- encoders and dummy anomalies for abnormal event detection in video.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Object-centric auto- encoders and dummy anomalies for abnormal event detection in video

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:42.686620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5afb4754-d76a-4171-aa2a-b71dc5b07348 · outbound

This paper cites Learning deep representations of appearance and motion for anomalous event detection.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Learning deep representations of appearance and motion for anomalous event detection

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:42.522184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9f49b659-6fd0-4852-b184-d502c63bed63 · outbound

This paper cites Netwalk: A flexible deep embedding approach for anomaly detection in dynamic networks.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Netwalk: A flexible deep embedding approach for anomaly detection in dynamic networks

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:42.392959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ef889112-5437-4753-a112-16a55ec4e250 · outbound

This paper cites Deep evidential regression.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Deep evidential regression

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:42.192188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e826d552-32b1-478a-bb26-7d08e35c3763 · outbound

This paper cites EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:42.041719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 30ff393f-ae39-495a-a283-3d49d6810ceb · outbound

This paper cites PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain

Reference 67

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:06:40.088115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6354d948-6b1e-48bb-bb43-04d80e4d311a · outbound

This paper cites Uncertainty-aware panoptic segmentation.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Uncertainty-aware panoptic segmentation

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:41.829965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5509b25a-7415-4b76-90d8-238d37bc8b3b · outbound

This paper cites Re- construction by inpainting for visual anomaly detection.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Re- construction by inpainting for visual anomaly detection

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:41.638213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aadb42d9-a155-4b38-93e8-ed6eab055cbc · outbound

This paper cites Learning deep representations of appearance and motion for anomalous event detection.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Learning deep representations of appearance and motion for anomalous event detection

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:41.430985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d46ed47b-2e62-4616-a565-6977f905722c · outbound

This paper cites Unsupervised deep anomaly detection in chest radiographs.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Unsupervised deep anomaly detection in chest radiographs

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:41.241405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 22c7f8ba-5c4b-427b-85ab-b3153b9fc490 · outbound

This paper cites Unsu- pervised anomaly detection with generative adversarial networks to guide marker discovery.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Unsu- pervised anomaly detection with generative adversarial networks to guide marker discovery

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:41.073881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 850fee28-b433-4d23-a551-a97b5805dffd · outbound

This paper cites f- anogan: Fast unsupervised anomaly detection with gen- erative adversarial networks.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation f- anogan: Fast unsupervised anomaly detection with gen- erative adversarial networks

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:40.885447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation de20b8e3-6334-4439-8d37-94b74b0f2dbc · outbound

This paper cites Ganomaly: Semi-supervised anomaly detection via adversarial training.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Ganomaly: Semi-supervised anomaly detection via adversarial training

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:40.648843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cce06f58-67b6-469b-904a-3ba0ee429cf8 · outbound

This paper cites Skip-ganomaly: Skip connected and adver- sarially trained encoder-decoder anomaly detection.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Skip-ganomaly: Skip connected and adver- sarially trained encoder-decoder anomaly detection

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:40.498815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1ae15082-a82c-4893-adad-240cccfbcd61 · outbound

This paper cites Diffusion models for medical anomaly detection.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Diffusion models for medical anomaly detection

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:40.396452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7531677e-8475-4433-9381-93c19df83534 · outbound

This paper cites Fast unsupervised brain anomaly detection and segmentation with diffusion models.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Fast unsupervised brain anomaly detection and segmentation with diffusion models

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:06:40.277518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4b698dc9-d402-4b2d-acdf-89ddd56996c5 · outbound

This paper cites DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-07T13:06:39.856136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e73c4392-f3e5-46e3-ad95-133f49c27e78 · outbound

This paper cites Anomaly Detection with Conditioned Denoising Diffusion Models.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Anomaly Detection with Conditioned Denoising Diffusion Models

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-07T13:06:39.937051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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