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Paper Citation Record · LEDGER

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples

As of 19 August 2026, this Paper Citation Record lists 100 of 113 outbound references and 1 inbound Pith citation observation for arXiv:2501.16971.

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

pith.paper-citation-record.v1
2501.16971 v1

Coverage vector

measured 100 of 113 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T05:29:03.215049Z

measured 101 of 101 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T05:32:44.340202Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

100 of 113 outbound references displayed

  • verified exact11
  • verified fuzzy17
  • unresolved68
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation aa6a340e-156d-421e-92ac-156a93cfc9e1 · outbound

This paper cites write newline.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples write newline

Reference 1

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source=arxiv_source observed=2026-08-10T05:29:02.655413Z digest=sha256:9756b9b0877fefef5ed8627a27b3689c2de01c18ab2b0f557df67eb75e52bac0

Observation d6a0cb5e-d387-42ba-a634-e9efaecf54a6 · outbound

This paper cites Adapting contrastive language-image pretrained (clip) models for out-of-distribution detection, 2023.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Adapting contrastive language-image pretrained (clip) models for out-of-distribution detection, 2023

Reference 2

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source=arxiv_source observed=2026-08-10T05:29:02.661909Z digest=sha256:7d0bd4955c44f76fe5646c6188942b1fedb016c54d34e7f3019db9d6b97176ea

Observation 314c9d90-5d9e-44fd-a48e-ea7cd22589de · outbound

This paper cites and Mian, A.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Mian, A

Reference 3

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source=arxiv_source observed=2026-08-10T05:29:02.667296Z digest=sha256:bdf1c589d58949771e052aee3ad6e20877fafa1ea199ab7803463ec50a36e60f

Observation 63ba13d2-8ce9-4cfb-920b-52488b668305 · outbound

This paper cites Blended diffusion for text-driven editing of natural images.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Blended diffusion for text-driven editing of natural images

Reference 4

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source=arxiv_source observed=2026-08-10T05:29:02.671569Z digest=sha256:bc5be6aafbab791b23492675223be6accdad95d6c1145a94dba7dcf9a67f8453

Observation 57475e1b-faea-402d-94af-072a9a026444 · outbound

This paper cites an unresolved cited work.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Unresolved cited work

Reference 5

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source=arxiv_source observed=2026-08-10T05:29:02.675602Z digest=sha256:ee76c6fd5fdb990e351b928ec201beae2c2b25948e948fa5a4ffd63e8b6a7e00

Observation d45fef7a-027a-4bf3-9b1f-bf991b400f86 · outbound

This paper cites and Boult, T.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Boult, T

Reference 6

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source=arxiv_source observed=2026-08-10T05:29:02.679438Z digest=sha256:da4b33a48d81ca64ac924f4a1b648ac2af2de650acb999ce4a7a67d400b31987

Observation 4cdea055-0e3f-4fdd-9de0-88ebe06f5969 · outbound

This paper cites and Boult, T.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Boult, T

Reference 7

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source=arxiv_source observed=2026-08-10T05:29:02.683401Z digest=sha256:b3786e2831cc11670e3a36988ce5759154b8f227e05c047e1fe077cc2f401911

Observation 44c18701-98fc-43ee-a369-a96291d4e1de · outbound

This paper cites Deep Nearest Neighbor Anomaly Detection.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Deep Nearest Neighbor Anomaly Detection

Reference 8

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source=arxiv_source observed=2026-08-10T05:29:02.687438Z digest=sha256:2341e3febf01d3f178639fcd3c32808c22114fb72fb1af2097a8bf8d4242ff8f

Observation 99545157-a303-46a4-8155-31159e1997b9 · outbound

This paper cites Mvtec ad--a comprehensive real-world dataset for unsupervised anomaly detection.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Mvtec ad--a comprehensive real-world dataset for unsupervised anomaly detection

Reference 9

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source=arxiv_source observed=2026-08-10T05:29:02.693607Z digest=sha256:987b64b2ba90ffe9295ee648d5336956428eb23cebdc4a549484c9eb77512827

Observation 0403a86f-2d9d-4c17-906e-b3b8bf7ca3ff · outbound

This paper cites Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks

Reference 10

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Observation 6f318043-90b3-40fc-82ad-3f04dd3f224d · outbound

This paper cites Brain tumor classification (mri), 2020.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Brain tumor classification (mri), 2020

Reference 11

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source=arxiv_source observed=2026-08-10T05:29:02.703616Z digest=sha256:90b5f170e62a4ac65aefa0e490eaa861a7da8867feea8495807014b9577b0ee0

Observation f75dfe65-3533-4906-a626-e2188b7febb0 · outbound

This paper cites and Zhang, Z.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Zhang, Z

Reference 12

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Observation 3bf41bd9-7178-4714-b31d-a43e60d32f7d · outbound

This paper cites Robust Out-of-distribution Detection for Neural Networks.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Robust Out-of-distribution Detection for Neural Networks

Reference 13

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Observation 4a4d8129-5caf-44dd-b1d2-fff53d2219d1 · outbound

This paper cites Atom: Robustifying out-of-distribution detection using outlier mining.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Atom: Robustifying out-of-distribution detection using outlier mining

Reference 14

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Observation 4324a6cd-df27-4ec2-9926-1d80f6bed3f1 · outbound

This paper cites P., Morrison, P., and Dao, L.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples P., Morrison, P., and Dao, L

Reference 15

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Observation 2d0966bb-3bcc-49f0-8b5a-a51303a36fbc · outbound

This paper cites Transformaly -- Two (Feature Spaces) Are Better Than One.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Transformaly -- Two (Feature Spaces) Are Better Than One

Reference 16

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local_arxiv, observed 2026-08-10T05:29:04.318249Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:02.724297Z digest=sha256:040316e06d1ee4d31c789a91adb2adeccebff25d4ab0d51c47fc970f4ec266d4

Observation 0b497d9d-73c8-4d69-aebb-29969929b617 · outbound

This paper cites and Vapnik, V.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Vapnik, V

Reference 17

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source=arxiv_source observed=2026-08-10T05:29:02.732173Z digest=sha256:7ee1079513454fdf8b442778266116a252b123667ed38429e9485b606bd01790

Observation f5183e84-8bba-4ad6-9dd0-cf91420bdf6f · outbound

This paper cites and Hein, M.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Hein, M

Reference 18

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Observation b7f102db-ae21-4ff3-bf94-f7e6e88268c3 · outbound

This paper cites T., and Shah, M.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples T., and Shah, M

Reference 19

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Observation b9e055dc-8c71-45ff-a721-0dafc94c53ba · outbound

This paper cites Imagenet: a large-scale hierarchical image database.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Imagenet: a large-scale hierarchical image database

Reference 20

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source=arxiv_source observed=2026-08-10T05:29:02.752816Z digest=sha256:fdc6b5ddf7663dc6bcf2537bbc53906947e9f4707428ab78d9c17f7b0b7ca6d6

Observation a55fcca0-f267-43a0-a600-4d3e885063a6 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 21

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Observation 96871c94-2689-438f-b148-a0c68907b521 · outbound

This paper cites and Nichol, A.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Nichol, A

Reference 22

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Observation aa76f562-fd3e-424c-8d5b-b31842b310b8 · outbound

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

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 23

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source=arxiv_source observed=2026-08-10T05:29:02.784749Z digest=sha256:8f4bc1c7fb4d2c15e4d0853f585c311b52235975f860ba1776f8d130585b5c4c

Observation 747a7fec-a962-43bf-a3c2-57268a5383f0 · outbound

This paper cites and Shearer, R.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Shearer, R

Reference 24

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source=arxiv_source observed=2026-08-10T05:29:02.789427Z digest=sha256:e1a31f9883abf41a0adaf9c439c60fb755de70a8f09d61f0a3d4e4d6324045cb

Observation 92863cf3-78d5-41ce-905c-17fe87d0cc83 · outbound

This paper cites VOS: Learning What You Don't Know by Virtual Outlier Synthesis.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples VOS: Learning What You Don't Know by Virtual Outlier Synthesis

Reference 25

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source=arxiv_source observed=2026-08-10T05:29:02.794449Z digest=sha256:cdc083971738e5b909a6d8ed5262d8bc27f80a0fbbb731002546738a6973a3e6

Observation c55a9007-cf7f-451f-918d-fc4f70bd22cc · outbound

This paper cites Dream the Impossible: Outlier Imagination with Diffusion Models.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Dream the Impossible: Outlier Imagination with Diffusion Models

Reference 26

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source=arxiv_source observed=2026-08-10T05:29:02.798955Z digest=sha256:d5274f0e66aa97e4fb4022f101cc19564233e654d1dc6d0bf9739b2ce8ad1672

Observation 63b14096-3eee-4270-840d-147a7a8de5b2 · outbound

This paper cites Sharif-STR at SemEval-2024 Task 1: Transformer as a Regression Model for Fine-Grained Scoring of Textual Semantic Relations.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Sharif-STR at SemEval-2024 Task 1: Transformer as a Regression Model for Fine-Grained Scoring of Textual Semantic Relations

Reference 27

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source=arxiv_source observed=2026-08-10T05:29:02.803339Z digest=sha256:d5f59534e1014ec9987b0e418f2dc64f23180000a33e774aeaf57939691bb46c

Observation 675c6d33-fba2-422b-8c54-1719dbcb627e · outbound

This paper cites Sharif-MGTD at SemEval-2024 Task 8: A Transformer-Based Approach to Detect Machine Generated Text.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Sharif-MGTD at SemEval-2024 Task 8: A Transformer-Based Approach to Detect Machine Generated Text

Reference 28

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source=arxiv_source observed=2026-08-10T05:29:02.808648Z digest=sha256:146d383f9479979a856bab7b0726bf5bf4d0cad33b789264f0b1b1e439f518a2

Observation c953953a-9a32-4dce-aed5-b530d961d1e3 · outbound

This paper cites Zero-shot out-of-distribution detection based on the pre-trained model clip.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Zero-shot out-of-distribution detection based on the pre-trained model clip

Reference 29

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Observation fc1dfc93-2c6c-4103-8b8b-b71d47c09f49 · outbound

This paper cites Exploring the limits of out-of-distribution detection.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Exploring the limits of out-of-distribution detection

Reference 30

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Observation 31aa161f-0af8-4e2d-8584-db50758b4119 · outbound

This paper cites Exploring the limits of out-of-distribution detection.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Exploring the limits of out-of-distribution detection

Reference 31

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Observation 99fd14b0-0b58-408a-b7bf-35325d6989a5 · outbound

This paper cites Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection

Reference 32

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Observation 39798dca-5f51-4aa2-b328-518ad8eb7d73 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Explaining and Harnessing Adversarial Examples

Reference 33

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source=arxiv_source observed=2026-08-10T05:29:02.828361Z digest=sha256:231e568d372b3fc5baba34484a100c173311dd32f296568b431a3aca20911e0a

Observation 2fac2930-7020-436d-84ae-41545ed9ab10 · outbound

This paper cites K., and Ng, W.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples K., and Ng, W

Reference 34

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Observation f374b1cf-3e5f-45ea-aa27-3b63f3faaaab · outbound

This paper cites G., and Weinberger, K.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples G., and Weinberger, K

Reference 35

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Observation 4bbf0a83-1940-4848-936d-28f86fe390d8 · outbound

This paper cites Deep residual learning for image recognition.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Deep residual learning for image recognition

Reference 36

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Observation 98416e3b-81b3-4163-9e7a-29a0a3f58704 · outbound

This paper cites and Gimpel, K.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Gimpel, K

Reference 37

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source=arxiv_source observed=2026-08-10T05:29:02.857879Z digest=sha256:af1b124c2665fb1c3b5b86aa740cbcb48a19f2a58b4480e385264abee3b50c65

Observation 173ca468-5374-4a95-8d2e-0a24a5adfcb8 · outbound

This paper cites Deep Anomaly Detection with Outlier Exposure.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Deep Anomaly Detection with Outlier Exposure

Reference 38

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source=arxiv_source observed=2026-08-10T05:29:02.862684Z digest=sha256:40f6f571a58d2cdea98cc0552ce3f960462f303dcacb537ca04f824898130dd9

Observation 99f87ab8-3200-4f6c-835a-39d859d1c92e · outbound

This paper cites Using pre-training can improve model robustness and uncertainty.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Using pre-training can improve model robustness and uncertainty

Reference 39

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source=arxiv_source observed=2026-08-10T05:29:02.866579Z digest=sha256:3d05caefde29c81584e4f57c27264b39e18bd4310cecb22251d0cbd30445a818

Observation bbce45cd-af43-4720-8a7f-c5f09174a07c · outbound

This paper cites GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

Reference 40

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source=arxiv_source observed=2026-08-10T05:29:02.870742Z digest=sha256:bce01092baabd4cd58ea291ea9b5b27b6e8e2b3998dc5be642a3eb3276ad970e

Observation 59e08871-9803-47c5-8a58-da86e6647d1d · outbound

This paper cites Denoising diffusion probabilistic models.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Denoising diffusion probabilistic models

Reference 41

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source=arxiv_source observed=2026-08-10T05:29:02.885393Z digest=sha256:ceebe137871cb276fb135685dbf5f61666ab9994b4f37b53acd106d631248d00

Observation 3c2df0da-4a66-4ade-b18e-f3d8f6f1e56e · outbound

This paper cites The power of few: Accelerating and enhancing data reweighting with coreset selection.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples The power of few: Accelerating and enhancing data reweighting with coreset selection

Reference 42

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source=arxiv_source observed=2026-08-10T05:29:02.888965Z digest=sha256:4c1bea7e98a4b532f9967396f92652744bdcb54fb2b630932f64c34f225670fe

Observation 8906e185-b4ca-41b9-be81-9bb560142c60 · outbound

This paper cites an unresolved cited work.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Unresolved cited work

Reference 43

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source=arxiv_source observed=2026-08-10T05:29:02.896406Z digest=sha256:86b07abcedc1a15cf9193ec0382caa453f2ac9cd9b605c47be7b3c29750d7ec6

Observation f8f5b3ca-b8a9-4e76-9f08-ff5279ae7e49 · outbound

This paper cites an unresolved cited work.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Unresolved cited work

Reference 44

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source=arxiv_source observed=2026-08-10T05:29:02.904754Z digest=sha256:9a91ec5a0ced0cac9737f7a245f1ced7eed994bbe8894866d451ebde73283f72

Observation e29e52a4-0991-4f95-bf22-3561bfbec1ec · outbound

This paper cites and Ortmeier, F.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Ortmeier, F

Reference 45

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source=arxiv_source observed=2026-08-10T05:29:02.922204Z digest=sha256:6184134e86b64c63426c0aa8ca55f7a49c6f6f3d6c982a7f8f9a15bb3b8037f8

Observation 89e0efc9-e10d-4167-9117-11cbafb8980b · outbound

This paper cites an unresolved cited work.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Unresolved cited work

Reference 46

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source=arxiv_source observed=2026-08-10T05:29:02.925977Z digest=sha256:b20210b41cea38f6203a38f24830c6848ae4cdcd4e56ec59b79c8c768ad3fcdd

Observation af29a76c-c2bd-41b1-a4f4-87e3af3b4c4a · outbound

This paper cites and Ramanan, D.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Ramanan, D

Reference 47

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source=arxiv_source observed=2026-08-10T05:29:02.929673Z digest=sha256:97e11207f010ac72ecee37208226e34de8d45b32353cde3c5f5c3057bf8ab4a1

Observation 364b4475-8b9e-4331-bbbd-cc14f34defcd · outbound

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

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Learning multiple layers of features from tiny images

Reference 48

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source=arxiv_source observed=2026-08-10T05:29:02.933680Z digest=sha256:0018d210d491e4bcaf5be9b408a323e016d12bc8231bc42c667fff5dedcfc2af

Observation f5dd9adc-0b31-4365-8c23-d7b029303232 · outbound

This paper cites and Cortes, C.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Cortes, C

Reference 49

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source=arxiv_source observed=2026-08-10T05:29:02.937529Z digest=sha256:368b51c17e9ac374627cecac6ba498670bfe6d2025c5750f205cf8b63717f04b

Observation 5e2d3d75-9056-41fa-9091-a4867a9e824a · outbound

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

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples A simple unified framework for detecting out-of-distribution samples and adversarial attacks

Reference 50

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source=arxiv_source observed=2026-08-10T05:29:02.941751Z digest=sha256:976a6fb6a9a543b69303b088bfbc7d8153d18899a580a20517a45d6d01cdab1e

Observation 55786025-20cb-456e-91c0-7a76fcfe67b5 · outbound

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

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples A simple unified framework for detecting out-of-distribution samples and adversarial attacks

Reference 51

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source=arxiv_source observed=2026-08-10T05:29:02.946345Z digest=sha256:523dedb9adbdc1edcd94e7cff934ca9e091530eceb34fcf585a674bfe85ce68d

Observation 645079b4-b06d-4a0b-956a-9bb9d9512236 · outbound

This paper cites Enhancing the reliability of out-of-distribution image detection in neural networks.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Enhancing the reliability of out-of-distribution image detection in neural networks

Reference 52

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source=arxiv_source observed=2026-08-10T05:29:02.960390Z digest=sha256:187a1f36badf2ebebba0f3ebeb49f3aff33006d1724c38f985243eda4f98b776

Observation 6d606755-26d7-4e30-9bc2-82ab39651927 · outbound

This paper cites Practical evaluation of adversarial robustness via adaptive auto attack, 2022.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Practical evaluation of adversarial robustness via adaptive auto attack, 2022

Reference 53

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raw_fallback, observed 2026-08-10T05:29:05.022627Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:02.964025Z digest=sha256:e4442bfe2304f7062d139f3bdffdbbe33a00f5dbbb432fd1a5c93dcbadedfd74

Observation fa5a6ced-49d7-4702-bfa6-901d9fde3dc8 · outbound

This paper cites Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images

Reference 54

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local_arxiv, observed 2026-08-10T05:29:03.983809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:02.970422Z digest=sha256:1902767820e9700813ae6663996793da8ecfab8343eb7c9377c8ed1fe466a844

Observation b1416192-c766-4685-983e-12de5ec1fe57 · outbound

This paper cites an unresolved cited work.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Unresolved cited work

Reference 55

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:02.974128Z digest=sha256:a16babff6f8f795edc8c80103ba1a1642079c87a32fe76cdcbe50017bbd0e641

Observation a82c5d7c-2049-492a-8a52-1178b7d46cdb · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 56

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source=arxiv_source observed=2026-08-10T05:29:02.980158Z digest=sha256:dd9d493f99b088f5d41f666a3e0b456dd6981fb3f7b4c80d27bb549138005fe5

Observation 00eb9dd2-30a9-451d-8b41-d44abc6ce80a · outbound

This paper cites Provably adversarially robust detection of out-of-distribution data (almost) for free.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Provably adversarially robust detection of out-of-distribution data (almost) for free

Reference 57

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verified fuzzy
raw_fallback, observed 2026-08-10T05:29:04.960762Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:02.989474Z digest=sha256:fc9ecf57cb0b10a03898f4446a6d562cd30a46f9b243c7d19f66f039c91c3e41

Observation 25af4625-439c-42b8-86d8-1c0798f57bb7 · outbound

This paper cites Sdedit: Guided image synthesis and editing with stochastic differential equations.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Sdedit: Guided image synthesis and editing with stochastic differential equations

Reference 58

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verified fuzzy
raw_fallback, observed 2026-08-10T05:29:04.935013Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:02.997134Z digest=sha256:28cc72ceac46aeda3ce2c2d825450295f879410fe1a2f88b496b47be43170019

Observation f665c403-ec94-428a-843f-44c1da7e5c51 · outbound

This paper cites Efficient Estimation of Word Representations in Vector Space.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Efficient Estimation of Word Representations in Vector Space

Reference 59

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source=arxiv_source observed=2026-08-10T05:29:03.001741Z digest=sha256:154695e6165f08149a1e7c037ee407ef658fc359c61dd26622082ab46084e5f9

Observation 3206d22b-3966-46c6-a3ea-46d3729b70a0 · outbound

This paper cites Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized Embeddings.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized Embeddings

Reference 60

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source=arxiv_source observed=2026-08-10T05:29:03.024904Z digest=sha256:8e1493ed79b4a5fdf30e5bce071b608e07e31ff0658259df2bdd9c8064584629

Observation a19c8da9-6502-486b-bca8-6c967901d80e · outbound

This paper cites D., Nafez, M., Madadi, M., Rezaee, S., Taghavi, Z.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples D., Nafez, M., Madadi, M., Rezaee, S., Taghavi, Z

Reference 61

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raw_fallback, observed 2026-08-10T05:29:04.905431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:03.045055Z digest=sha256:1abdf7aca174a97ead17232788a79fa62279599b5fe0ccd01b6a2c8e445dd810

Observation 6e84eaec-f202-4fbd-abe5-ec86cddc10b6 · outbound

This paper cites Fake It Till You Make It: Towards Accurate Near-Distribution Novelty Detection.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Fake It Till You Make It: Towards Accurate Near-Distribution Novelty Detection

Reference 62

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metadata mismatch
local_arxiv, observed 2026-08-10T05:29:03.934753Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:03.060490Z digest=sha256:3cf5e81c3340e0718d9353ecc2f96eb0916fa15ed6f2a123e11d46b1f13fbe1c

Observation fb0f1530-0924-469d-8ee7-abb1feaf24bd · outbound

This paper cites R., Taghavi, Z.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples R., Taghavi, Z

Reference 63

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raw_fallback, observed 2026-08-10T05:29:04.875999Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:03.064770Z digest=sha256:a0e277a1f06d198a9d4c1c2e89a9c87055864d583aef6c470dd6b64a41113ff4

Observation a975139a-57a6-45cd-bd79-65cf0df172f9 · outbound

This paper cites B., Azizmalayeri, M., Habibi, J., Sabokrou, M., and Rohban, M.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples B., Azizmalayeri, M., Habibi, J., Sabokrou, M., and Rohban, M

Reference 64

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raw_fallback, observed 2026-08-10T05:29:04.861752Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:03.068203Z digest=sha256:4ff280808c514e5e64fab4df1d90b17b740d8e9d8b67b87b31ab361872d54e2e

Observation 763ec3b2-7ab1-41dd-8224-7928c5a53c28 · outbound

This paper cites Seeking Next Layer Neurons' Attention for Error-Backpropagation-Like Training in a Multi-Agent Network Framework.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Seeking Next Layer Neurons' Attention for Error-Backpropagation-Like Training in a Multi-Agent Network Framework

Reference 65

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local_arxiv, observed 2026-08-10T05:29:03.917131Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:03.071894Z digest=sha256:da0e14bc027270fbc1b75213e23f1c3d6e255f2a4e75eb3ee441b4fe0d6bef29

Observation 4798355d-b33d-496a-898d-3a2487b66f4e · outbound

This paper cites F., Oh, S.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples F., Oh, S

Reference 66

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raw_fallback, observed 2026-08-10T05:29:04.847621Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:03.076169Z digest=sha256:27b14d9684c16006ee8477f9581f2a89a2a13720262c707e76bb13a0e051319c

Observation 41a60ac9-e3f9-4a07-b141-9658cf59f1b6 · outbound

This paper cites Social Biases through the Text-to-Image Generation Lens.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Social Biases through the Text-to-Image Generation Lens

Reference 67

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source=arxiv_source observed=2026-08-10T05:29:03.081220Z digest=sha256:74a15448fb8f77d431cef463f14da58fd4e63f1133df5117342ee20014814720

Observation ec2a6773-4aa4-4257-968e-3bdadad42507 · outbound

This paper cites Columbia object image library: Coil-100.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Columbia object image library: Coil-100

Reference 68

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raw_fallback, observed 2026-08-10T05:29:04.834506Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:03.087480Z digest=sha256:bdea3984e50e54672b1ed7e987097fb57a1a1f4e304ac7844288f772207edb04

Observation 77203af4-20d1-4854-89d4-b9ee2bee6f0b · outbound

This paper cites GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Reference 69

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source=arxiv_source observed=2026-08-10T05:29:03.092413Z digest=sha256:2efb8949d4a9eb18588509168965be8a3d0d5b700b3b5a2d01e0cd011c1d7558

Observation 52f4701d-6039-4996-98c1-efeb34a2e18b · outbound

This paper cites Brain tumor mri dataset, 2021.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Brain tumor mri dataset, 2021

Reference 70

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source=arxiv_source observed=2026-08-10T05:29:03.096709Z digest=sha256:9348a38ab3b892783bce35ea360336c000f8dd12e53e6e1020fb86edc6e6d343

Observation 320479c3-1deb-4b52-a7e1-0c7dae7d0ce2 · outbound

This paper cites and Zisserman, A.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Zisserman, A

Reference 71

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source=arxiv_source observed=2026-08-10T05:29:03.100618Z digest=sha256:e21dd6a08552e64777c75f99865fd36e4e3156a88a449188158549e7ce4822cc

Observation c445b593-1911-43d5-a122-601854e8e21b · outbound

This paper cites Robustness and accuracy could be reconcilable by (proper) definition.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Robustness and accuracy could be reconcilable by (proper) definition

Reference 72

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raw_fallback, observed 2026-08-10T05:29:04.797478Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:03.104908Z digest=sha256:46acce5ebbbbc0453277a2c6c4028a8fc1bd491cb17377dfb8782fd1e12219a5

Observation cf0212ad-4d86-4414-9d49-5feb13468248 · outbound

This paper cites One-Class Classification: A Survey.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples One-Class Classification: A Survey

Reference 73

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source=arxiv_source observed=2026-08-10T05:29:03.112788Z digest=sha256:7f1a5a04d03e9a432a1c3cf598ee8b34362ce57744e814743c74a76e0939cbdc

Observation 436bbf64-c79e-4192-b09c-167fa9bf3321 · outbound

This paper cites W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al

Reference 74

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Observation ce6b99f7-ffcb-470a-bda5-65deafd7cec0 · outbound

This paper cites H allu S afe at S em E val-2024 task 6: An NLI -based approach to make LLM s safer by better detecting hallucinations and overgeneration mistakes.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples H allu S afe at S em E val-2024 task 6: An NLI -based approach to make LLM s safer by better detecting hallucinations and overgeneration mistakes

Reference 75

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:03.120064Z digest=sha256:bfe6168b1530578a115a260265f6d9db6f9d1c9c68a17df12bb414d7ad65bf91

Observation fd57d6e5-0317-4439-802f-5728f5ba4066 · outbound

This paper cites M., Taghavi, Z., and Sameti, H.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples M., Taghavi, Z., and Sameti, H

Reference 76

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doi, observed 2026-08-10T05:29:03.311186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:03.123435Z digest=sha256:08c50183b2cc801d62b98d7874c92cf9d89088924c0103f1a51bfa930b6530bc

Observation c6218149-d55e-4e53-a367-91607f834d62 · outbound

This paper cites Mean-Shifted Contrastive Loss for Anomaly Detection.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Mean-Shifted Contrastive Loss for Anomaly Detection

Reference 77

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local_arxiv, observed 2026-08-10T05:29:03.732014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:03.127200Z digest=sha256:2341ec9b5af0cd02f85793371d6ed5c0d5d69525eb6b41e5f8fa54b7c6da614f

Observation 3a07f206-d92f-4a86-adba-86bbc180112d · outbound

This paper cites Panda: Adapting pretrained features for anomaly detection and segmentation.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Panda: Adapting pretrained features for anomaly detection and segmentation

Reference 78

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:03.130972Z digest=sha256:d9ae496d48c1ab8a2009bbac0514d6612ff7b5eed9cd8d1977e658834abbbbe5

Observation f34b201a-142d-4113-9b36-327ac691bc9f · outbound

This paper cites A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection

Reference 79

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source=arxiv_source observed=2026-08-10T05:29:03.134515Z digest=sha256:a735cad4a74f93e6a12cfb5bd396d45edc4c706f29345b9f668113a99face4bb

Observation 61a9a0c1-b01d-48b5-9020-dd7b0580a9c3 · outbound

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

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples High-resolution image synthesis with latent diffusion models

Reference 80

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source=arxiv_source observed=2026-08-10T05:29:03.138396Z digest=sha256:29fa3d5c191be497747a1c14c6c238079c215b2a25b2fc9138db77592cd038e0

Observation 8f225556-84e7-4b98-9e17-0f7f2bbcb17e · outbound

This paper cites Towards total recall in industrial anomaly detection, 2021.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Towards total recall in industrial anomaly detection, 2021

Reference 81

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:03.141906Z digest=sha256:067b179d076b4ae68c88b9ef7a3a4a64cf5cabeaf4fab271418be61c5748f0ae

Observation 936f94a5-21ce-4561-852e-d68e838e8a4e · outbound

This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 82

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source=arxiv_source observed=2026-08-10T05:29:03.145397Z digest=sha256:e0d880150e2a78916b20bccbb58ed6e0e8508fb17553b5e48615bb2b78572a05

Observation 99aba3dc-84b1-4867-8d89-d489c4cf4808 · outbound

This paper cites A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges

Reference 83

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source=arxiv_source observed=2026-08-10T05:29:03.149066Z digest=sha256:f018c1dcfdd8638dc07b73338c0a0e474df6b1ccb77f9e555d14f10d2081fe2a

Observation eb8c6117-d745-4369-b662-b45d36479ddd · outbound

This paper cites H., and Rabiee, H.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples H., and Rabiee, H

Reference 84

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raw_fallback, observed 2026-08-10T05:29:04.695983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:03.152894Z digest=sha256:acaefd1fd7487a5ee85d4587bbaaf62ab28ebc0c625d250f145d992776415c49

Observation 06fb3c8e-8a96-41df-ad50-46daea1f52ba · outbound

This paper cites Adversarially robust generalization requires more data.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Adversarially robust generalization requires more data

Reference 85

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:03.156368Z digest=sha256:7451709bfa4b513dff91e39d9fde3b608419d4fdff0500fbecaf64c7fa349db9

Observation 61cc1bb2-225b-48c4-b3c0-64f19acf95ba · outbound

This paper cites LAION-5B: An open large-scale dataset for training next generation image-text models.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples LAION-5B: An open large-scale dataset for training next generation image-text models

Reference 86

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source=arxiv_source observed=2026-08-10T05:29:03.160647Z digest=sha256:cb32dbee94794f574a5ad0f90e73f3af9539d165c6e22891c86ad0f42a4ed53a

Observation e941add0-e783-4240-b195-39e78bbbc2c7 · outbound

This paper cites Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?

Reference 87

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source=arxiv_source observed=2026-08-10T05:29:03.165821Z digest=sha256:a8d72191677959e1d63e6b056737dc59be01b2abe3055db6aa77539794568e84

Observation 856f7930-1917-4323-af30-a7b037572029 · outbound

This paper cites C., and Patel, V.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples C., and Patel, V

Reference 88

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:03.169611Z digest=sha256:790b5726f8eee6758765e8a36bab178e810f9c6f15e60641af21106ae838cf8c

Observation 8453bfc6-feca-4e94-bcc8-59f60af42946 · outbound

This paper cites C., and Patel, V.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples C., and Patel, V

Reference 89

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:03.172939Z digest=sha256:c816d5bcb76ada445bc1ad02b756f18c9fc3d4c6ba57491a2981661d8bc664b4

Observation 2bf513be-996f-4c87-9c3d-2788deb0d790 · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Deep unsupervised learning using nonequilibrium thermodynamics

Reference 90

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source=arxiv_source observed=2026-08-10T05:29:03.176307Z digest=sha256:9b0aa8f2182437d89f15df6b7ea0a3c8ce8f5df103ab43b2700f18692f59b9f3

Observation 67148f30-9838-4a04-b21d-93a5e9e95193 · outbound

This paper cites Disentangling adversarial robustness and generalization.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Disentangling adversarial robustness and generalization

Reference 91

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raw_fallback, observed 2026-08-10T05:29:04.638738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:03.181320Z digest=sha256:61ec033a4b8687b16ec2f6a4d7c66d31b5dbfb99da4192c9bbe1ec78b4ec7eba

Observation 7e72011e-5b9a-45b6-b946-134a606644e1 · outbound

This paper cites Intriguing properties of neural networks.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Intriguing properties of neural networks

Reference 92

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source=arxiv_source observed=2026-08-10T05:29:03.185045Z digest=sha256:bb564f1ba830f2c04dfce9c11180e493e41845e515db065d4a0677d00c3395d3

Observation 57287427-28ac-4926-ad80-6744ec409137 · outbound

This paper cites Csi: Novelty detection via contrastive learning on distributionally shifted instances.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Csi: Novelty detection via contrastive learning on distributionally shifted instances

Reference 93

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-10T05:29:03.189656Z digest=sha256:98205c323c7432ef06770e6124ea6277939f9907a6ca04e6dc0d6c6b10b806e2

Observation 134118af-93c3-4689-bb11-eb40fe6397fe · outbound

This paper cites Backdooring Outlier Detection Methods: A Novel Attack Approach.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Backdooring Outlier Detection Methods: A Novel Attack Approach

Reference 94

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local_arxiv, observed 2026-08-10T05:29:03.663525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:03.193034Z digest=sha256:6c5284119d5e6fe830b9fc40dbeb34a95f4c5d5beb9c27ae6688e61e6ce30263

Observation 7ac558ab-7b9a-4c2b-b66a-cec6e7e30a8a · outbound

This paper cites H., Sadraei Javaheri, M.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples H., Sadraei Javaheri, M

Reference 95

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doi, observed 2026-08-10T05:29:04.618639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:03.196712Z digest=sha256:f0f3c930d43e0cc6cf99297d58f72b27a54049dbadb558b5bb78a0ea1c41e86f

Observation f9c7de39-ed87-4cf8-a5e2-091d6f580d91 · outbound

This paper cites Imaginations of WALL-E : Reconstructing Experiences with an Imagination-Inspired Module for Advanced AI Systems.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Imaginations of WALL-E : Reconstructing Experiences with an Imagination-Inspired Module for Advanced AI Systems

Reference 96

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local_arxiv, observed 2026-08-10T05:29:03.650773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:03.200201Z digest=sha256:19300464c6b0cd122b04e19a71f9a82311e3431061c005fe4f7cd60499630e03

Observation af65d2e9-43d3-4911-9bfa-b5736b756549 · outbound

This paper cites A Change of Heart: Improving Speech Emotion Recognition through Speech-to-Text Modality Conversion.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples A Change of Heart: Improving Speech Emotion Recognition through Speech-to-Text Modality Conversion

Reference 97

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local_arxiv, observed 2026-08-10T05:29:03.635726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:03.203813Z digest=sha256:f77955ec440d04fb25a7da6f99fee54393cc8b2857d180ca0f4a07dafdbecf73

Observation 41d86130-b816-4e0d-b796-0ccec1620b72 · outbound

This paper cites Non-Parametric Outlier Synthesis.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Non-Parametric Outlier Synthesis

Reference 98

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source=arxiv_source observed=2026-08-10T05:29:03.207388Z digest=sha256:df5d8560d99e615b48a283d6b65e61576b46ac4349e92df5d7476fb55f05236e

Observation 93c839d4-7541-4974-9ebf-d0574c0f5b51 · outbound

This paper cites Non-parametric outlier synthesis, 2023 b.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples Non-parametric outlier synthesis, 2023 b

Reference 99

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raw_fallback, observed 2026-08-10T05:29:04.608718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-10T05:29:03.211620Z digest=sha256:d7fbc7bf9009b425a6bf2f3e3dd7ec945a76eaf93349ca2316639440cb47ef31

Observation 8a1e89f8-897a-4003-b6e6-b21aea4a29e6 · outbound

This paper cites and Hinton, G.

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples and Hinton, G

Reference 100

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-10T05:29:03.215049Z digest=sha256:a95e74c44d5779d1fabb071a5239ba30da28daa28cd746488ad950f3c4a11bf3

Pith citing papers

Observation 78ae8ff0-439e-4ea3-84f2-e5bbd487afb1 · inbound

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection cites this paper.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples

Reference 57

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T05:32:44.340202Z digest=sha256:2b5150b189b6a0b0574bc624031e3536d8780072a51928ade7d883215b0e5dd5