Paper Citation Record · LEDGER

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection

As of 10 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2501.15434.

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

pith.paper-citation-record.v1
2501.15434 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:22:06.253251Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 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

48 of 48 outbound references displayed

verified exact2
verified fuzzy9
unresolved36
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 93b75912-bb06-44c3-9254-768cff3aa379 · outbound

This paper cites Towards open world recognition.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Towards open world recognition

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:22:07.159378Z

Source-reported events for the cited work

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

Observation 0bfeeb0f-d249-44a7-84f2-01df73951ba6 · outbound

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

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks

Reference 5

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Observation 0c9212fd-7f17-44fb-8bcd-2bb563236325 · outbound

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

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T14:22:06.056677Z

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Observation 4bcc90d7-3bff-4d7a-a521-8e44438bdc9c · outbound

This paper cites an unresolved cited work.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Unresolved cited work

Reference 11

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Observation 397491d9-1235-4194-b461-63f28e4e428c · outbound

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

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Fake It Till You Make It: Towards Accurate Near-Distribution Novelty Detection

Reference 13

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Observation 7e53991a-25dc-41cd-bd56-5b841fa8b5e2 · outbound

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

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Dream the Impossible: Outlier Imagination with Diffusion Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T14:22:06.091150Z

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Observation 4be99cbb-64da-4e54-b198-b84a74878f43 · outbound

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

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection LAION-5B: An open large-scale dataset for training next generation image-text models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T14:22:06.095758Z

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Observation 5ba9e8ab-751d-414b-beef-d425ae724db0 · outbound

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

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection VOS: Learning What You Don't Know by Virtual Outlier Synthesis

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T14:22:06.100385Z

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Observation 8f986cd7-9e1b-47dd-890d-2c1f88a6ce4d · outbound

This paper cites an unresolved cited work.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Unresolved cited work

Reference 19

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Observation 4e33d504-732b-469d-bb44-5ed8312e1831 · outbound

This paper cites Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation

Reference 20

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Observation 076c9a90-614e-4389-b533-5f205d1898b3 · outbound

This paper cites Data Augmentation in Training CNNs: Injecting Noise to Images.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Data Augmentation in Training CNNs: Injecting Noise to Images

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T14:22:06.123855Z

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Observation 75afd6a0-eb6c-47e3-b598-d700bb62e6a7 · outbound

This paper cites Improved Regularization of Convolutional Neural Networks with Cutout.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Improved Regularization of Convolutional Neural Networks with Cutout

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T14:22:06.128649Z

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Observation d2758c1a-eef7-4cb7-a982-35e5cc8d83d7 · outbound

This paper cites Learning and Evaluating Representations for Deep One-class Classification.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Learning and Evaluating Representations for Deep One-class Classification

Reference 23

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Observation e02abff4-2c22-40f8-8220-39c3c6504ae4 · outbound

This paper cites Contrastive Predictive Coding for Anomaly Detection.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Contrastive Predictive Coding for Anomaly Detection

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:22:06.683345Z

Source-reported events for the cited work

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

Observation 7001a671-b28e-4ab7-96fc-7665268e9cd9 · outbound

This paper cites Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion, Philippe Weinzaepfel, and Diane Larlus.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion, Philippe Weinzaepfel, and Diane Larlus

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:22:07.116213Z

Source-reported events for the cited work

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

Observation 7f090f0f-2dfa-4bde-b815-8b4df1d69426 · outbound

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

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Transformaly -- Two (Feature Spaces) Are Better Than One

Reference 26

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Observation 5ae60844-8639-41c0-8caa-56408adf1402 · outbound

This paper cites Killing it with zero-shot: Adversarially robust novelty detection.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Killing it with zero-shot: Adversarially robust novelty detection

Reference 27

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Observation 096f478d-4ff5-45cc-b046-db193148e275 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Explaining and Harnessing Adversarial Examples

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T14:22:06.156271Z

Source-reported events for the cited work

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Observation 3870fd2e-79aa-4fa5-a368-0aa4b2001ef3 · outbound

This paper cites Hamprecht.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Hamprecht

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:22:07.101944Z

Source-reported events for the cited work

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

Observation 1dd89000-f357-4d21-b432-65405021a6eb · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 32

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Observation 01e8a829-351b-43c1-bc35-7615d757d854 · outbound

This paper cites Robustness May Be at Odds with Accuracy.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Robustness May Be at Odds with Accuracy

Reference 33

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Observation c3ce0f52-6fc7-49a4-9ecf-1ccb019afa4e · outbound

This paper cites FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T14:22:06.184692Z

Source-reported events for the cited work

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Observation 739344ff-8788-4451-8a98-9843aa8e2408 · outbound

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

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T14:22:06.189504Z

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Observation 0efc123e-4baf-4486-80e8-afa3c5f35e7e · outbound

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

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized Embeddings

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T14:22:06.194249Z

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Observation 2d11e08a-2603-4e42-bb18-f4440da695b4 · outbound

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

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Seeking Next Layer Neurons' Attention for Error-Backpropagation-Like Training in a Multi-Agent Network Framework

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T14:22:06.198887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75c28590-2dec-4d0a-af2e-d6ab86b43528 · outbound

This paper cites Killing it with zero-shot: Adversarially robust novelty detection.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Killing it with zero-shot: Adversarially robust novelty detection

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:22:07.087729Z

Source-reported events for the cited work

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

Observation 2766ee91-fe64-4302-93e7-88f11f79598a · outbound

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

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection A Change of Heart: Improving Speech Emotion Recognition through Speech-to-Text Modality Conversion

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-10T14:22:06.207677Z

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Observation fb1a4a4a-9650-41d6-bde3-3e82ed7ebf54 · outbound

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

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Sharif-STR at SemEval-2024 Task 1: Transformer as a Regression Model for Fine-Grained Scoring of Textual Semantic Relations

Reference 41

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Observation fbda86ff-4dc4-43d4-bb12-81773194531a · outbound

This paper cites Understanding and Mitigating the Tradeoff Between Robustness and Accuracy.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Understanding and Mitigating the Tradeoff Between Robustness and Accuracy

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-10T14:22:06.223318Z

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Observation d5bc5a93-2acb-4cfa-9772-cb85cda4a559 · outbound

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Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:22:07.071911Z

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Observation f5bc06a9-f88c-425d-a580-5603fcb492b9 · outbound

This paper cites Recent standard AD methods can be categorized into two types: transfer learning based and CL based methods.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Recent standard AD methods can be categorized into two types: transfer learning based and CL based methods

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:22:07.056604Z

Source-reported events for the cited work

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

Observation 4a8f43be-b40f-4c0d-9bee-794762885b12 · outbound

This paper cites Additionally, we considered A(x) + A′(x) as another alternative.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Additionally, we considered A(x) + A′(x) as another alternative

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:22:07.042043Z

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Observation aea4ca46-8615-469f-acea-85ed52e8d6ab · outbound

This paper cites Training Computational Cost.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Training Computational Cost

Reference 128

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Observation ec7140c4-7839-4722-8920-2172796d5763 · outbound

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

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Backdooring Outlier Detection Methods: A Novel Attack Approach

Reference 269

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Observation c5bb212a-1705-479e-92db-fb42d1a2c690 · outbound

This paper cites Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks

Reference 2007

Resolution: unresolved; no resolver link, observed 2026-08-10T14:22:06.170499Z

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Observation d2496df5-11fa-4189-b76e-891fbaa607d4 · outbound

This paper cites Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC).

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

Reference 2009

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Observation f4103406-f694-4080-a31d-e6e54fca8a40 · outbound

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Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding

Reference 2013

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Observation 663357ce-385c-4727-8774-e5babf5b9a0f · outbound

This paper cites Composite Adversarial Attacks.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Composite Adversarial Attacks

Reference 2014

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:22:06.467382Z

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Observation 7606b05f-0546-426a-998f-44138a8f4309 · outbound

This paper cites One-Class Classification: A Survey.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection One-Class Classification: A Survey

Reference 2015

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Observation c709419d-d3ac-4d8d-a2b0-9b14f06a9705 · outbound

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Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Imagenet: A large-scale hierarchical image database

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:22:07.145396Z

Source-reported events for the cited work

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Observation cb4823dd-bb30-4566-93d2-0dab803e5bc6 · outbound

This paper cites Unsupervised learning of visual representations by solving jigsaw puzzles.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Unsupervised learning of visual representations by solving jigsaw puzzles

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:22:07.130909Z

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Observation c9f2993c-ce73-44b5-bec7-3e636ee315b9 · outbound

This paper cites Deep Anomaly Detection with Outlier Exposure.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Deep Anomaly Detection with Outlier Exposure

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-10T14:22:06.104871Z

Source-reported events for the cited work

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Observation 43333238-0b4c-46a5-8e45-4abccb3a0bf0 · outbound

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Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Generalized Out-of-Distribution Detection: A Survey

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-10T14:22:06.081295Z

Source-reported events for the cited work

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Observation 6ac70bbb-a88a-490d-b621-8b1e3de90a68 · outbound

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Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Deep Nearest Neighbor Anomaly Detection

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-10T14:22:06.036571Z

Source-reported events for the cited work

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Observation 52ec0483-9b4c-4a5f-9eb4-78f315b7c76f · outbound

This paper cites Recent Advances in Adversarial Training for Adversarial Robustness.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Recent Advances in Adversarial Training for Adversarial Robustness

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-10T14:22:06.051469Z

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Observation d7f9e3b5-09c5-46d1-915b-423168e68758 · outbound

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

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Robust Out-of-distribution Detection for Neural Networks

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-10T14:22:06.041522Z

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Observation 9139af62-b957-4494-8ef2-48b530ee6696 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection mixup: Beyond Empirical Risk Minimization

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-10T14:22:06.109833Z

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Observation 8d395cfc-5fad-40c8-bef6-0de38cc4d824 · outbound

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

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection Mean-Shifted Contrastive Loss for Anomaly Detection

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-10T14:22:06.066766Z

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Pith citing papers

No inbound Pith citation observations are available.