Paper Citation Record · LEDGER

Killing it with Zero-Shot: Adversarially Robust Novelty Detection

As of 10 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2501.15271.

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

pith.paper-citation-record.v1
2501.15271 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:30:29.473393Z

measured 44 of 44 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:30:29.270425Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T14:30:29.772103Z

Reference resolution

43 of 43 outbound references displayed

verified exact0
verified fuzzy24
unresolved17
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation abdd564d-bbad-441a-8179-11e8bc7eb296 · outbound

This paper cites Killing it with Zero-Shot: Adversarially Robust Novelty Detection.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Killing it with Zero-Shot: Adversarially Robust Novelty Detection

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T14:30:29.779054Z

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 981667e0-f26b-43b4-b6df-b1663fe11ca1 · outbound

This paper cites Various self-supervised approaches have been introduced in ND to learn the normal sample distribution.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Various self-supervised approaches have been introduced in ND to learn the normal sample distribution

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:30:30.160578Z

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 1c84a1d2-3a35-4bf7-ad83-2b663d396bcd · outbound

This paper cites In ND, our focus is on a specialized training set that comprises solely normal, unlabeled samples.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection In ND, our focus is on a specialized training set that comprises solely normal, unlabeled samples

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:30:30.130024Z

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 362f25d1-a685-4713-a9c8-974976654898 · outbound

This paper cites Initialization: Feature Extraction Our methodology begins with the critical step of feature ex- traction, setting the stage for all downstream operations.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Initialization: Feature Extraction Our methodology begins with the critical step of feature ex- traction, setting the stage for all downstream operations

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:30:30.115533Z

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 7490cd58-3c05-4c99-973a-284caf2f296a · outbound

This paper cites an unresolved cited work.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Unresolved cited work

Reference 5

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T14:30:30.100482Z

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 3c7b4e96-dcef-4c05-b91a-e54844b7c3df · outbound

This paper cites Our method performs well in AUROC metrics and strongly resists adversarial attacks.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Our method performs well in AUROC metrics and strongly resists adversarial attacks

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:30:30.084955Z

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 365a6758-068f-45dd-a219-ea5907f7598e · outbound

This paper cites Deep neural networks are easily fooled: High confidence pre- dictions for unrecognizable images,.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Deep neural networks are easily fooled: High confidence pre- dictions for unrecognizable images,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:30:30.039762Z

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 eee2b0f3-b788-4569-bd68-8112eb02df9d · outbound

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

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Mean-Shifted Contrastive Loss for Anomaly Detection

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T14:30:29.305072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b753a99-95a7-4a3f-8539-923ea53f8c85 · outbound

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

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Transformaly -- Two (Feature Spaces) Are Better Than One

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T14:30:29.310029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8605b80e-77e2-4cc3-8489-d039baed0f12 · outbound

This paper cites Ad- versarially robust one-class novelty detection,.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Ad- versarially robust one-class novelty detection,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:30:30.069993Z

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 72cb72ea-2d88-4cf8-947a-ac54973daa1f · outbound

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

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T14:30:29.319424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f442d91-8b04-42a9-9c23-78eb2db7dc74 · outbound

This paper cites Robustness of autoencoders for anomaly detection under adversarial impact,.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Robustness of autoencoders for anomaly detection under adversarial impact,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:30:30.054283Z

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 2ca85b56-e7ec-4862-80aa-2550193da593 · outbound

This paper cites One-Class Classification: A Survey.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection One-Class Classification: A Survey

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T14:30:29.329333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b6b22fe-d5b6-41bc-bb69-17d191f9a3db · outbound

This paper cites Deep Nearest Neighbor Anomaly Detection.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Deep Nearest Neighbor Anomaly Detection

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T14:30:29.368186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d665660-5e41-4b29-8069-fe4c665eea0f · outbound

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

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:30:30.024734Z

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 7d342865-ec42-46e0-8ad7-ea6a2c9eeced · outbound

This paper cites EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T14:30:29.343552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21a54cac-189e-4d82-911c-b55fb5736950 · outbound

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

Killing it with Zero-Shot: Adversarially Robust Novelty Detection A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T14:30:29.348421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ccdb676-5b29-49ab-8484-1b96d078817d · outbound

This paper cites Papajorgji, and Panos M.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Papajorgji, and Panos M

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:30:30.009580Z

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 c4eab3a0-2c79-4506-aabf-d7d825836509 · outbound

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

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Csi: Novelty detection via contrastive learning on distributionally shifted instances,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:30:29.994552Z

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 348b51e7-edc2-484a-8cc4-13d7d9b8ff43 · outbound

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

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Fake It Till You Make It: Towards Accurate Near-Distribution Novelty Detection

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T14:30:29.363163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1523143-12f0-4aa0-aa20-7024ec46dfda · outbound

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

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Seeking Next Layer Neurons' Attention for Error-Backpropagation-Like Training in a Multi-Agent Network Framework

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T14:30:29.402441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e5bb389-be00-401b-bcfe-3fc864133b3b · outbound

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

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Panda: Adapting pretrained features for anomaly detection and segmentation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:30:29.979829Z

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 91c6cec6-88d7-43a9-97bb-8abc4baa75c9 · outbound

This paper cites Open-set adversarial defense with clean-adversarial mutual learning,.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Open-set adversarial defense with clean-adversarial mutual learning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:30:29.964954Z

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 d0a73ce8-e0e9-49dd-bcd9-8f243af79c99 · outbound

This paper cites Rodeo: Robust outlier detection via ex- posing adaptive out-of-distribution samples,.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Rodeo: Robust outlier detection via ex- posing adaptive out-of-distribution samples,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:30:29.950674Z

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 a809b81b-f8ba-4417-a93f-f32c870f406d · outbound

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

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized Embeddings

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T14:30:29.387333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb0ab26f-ba95-4a2e-b5c6-aa5e7bbff0f5 · outbound

This paper cites Universal novelty detection through adaptive contrastive learning,.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Universal novelty detection through adaptive contrastive learning,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:30:29.934183Z

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 d6bfb1e7-0dd4-4379-8dcd-ac9554dc432f · outbound

This paper cites Scanning trojaned models using out- of-distribution samples,.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Scanning trojaned models using out- of-distribution samples,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:30:29.919064Z

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 9e4e3008-1f68-4f90-92b1-95dee6b790d5 · outbound

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

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Sharif-MGTD at SemEval-2024 Task 8: A Transformer-Based Approach to Detect Machine Generated Text

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T14:30:29.435947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12d0b69a-165f-48f3-b84a-95a21d692f87 · outbound

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

Killing it with Zero-Shot: Adversarially Robust Novelty Detection A Change of Heart: Improving Speech Emotion Recognition through Speech-to-Text Modality Conversion

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T14:30:29.407338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3074691-9785-468d-8550-d1fe7ced2a88 · outbound

This paper cites During each itera- tion, the adversarial noise is constrained within an ℓ∞-ball of radius ϵ: x∗ 0 = x, x∗ t+1 = x∗ t + α.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection During each itera- tion, the adversarial noise is constrained within an ℓ∞-ball of radius ϵ: x∗ 0 = x, x∗ t+1 = x∗ t + α

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:30:30.145787Z

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 ec9401d0-daa7-4450-a1c3-b03e0941e384 · outbound

This paper cites HalluSafe at SemEval-2024 task 6: An NLI-based approach to make LLMs safer by better detecting hallucinations and over- generation mistakes,.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection HalluSafe at SemEval-2024 task 6: An NLI-based approach to make LLMs safer by better detecting hallucinations and over- generation mistakes,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:30:29.903899Z

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 c4202e55-9382-4f6c-b3f5-528ffd7f90be · outbound

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

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Imaginations of WALL-E : Reconstructing Experiences with an Imagination-Inspired Module for Advanced AI Systems

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T14:30:29.417005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52151135-db61-4cb8-9b9f-35c4b3fb907c · outbound

This paper cites Ebhaam at SemEval-2023 task 1: A CLIP-based approach for com- paring cross-modality and unimodality in visual word sense disambiguation,.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Ebhaam at SemEval-2023 task 1: A CLIP-based approach for com- paring cross-modality and unimodality in visual word sense disambiguation,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:30:29.888162Z

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 9ae65b69-bb3c-4ef2-bdee-529ea571e934 · outbound

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

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Backdooring Outlier Detection Methods: A Novel Attack Approach

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T14:30:29.426409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3265cf2d-01eb-48e5-af05-41b1cda8dcef · outbound

This paper cites NIMZ at SemEval-2024 task 9: Evaluating methods in solving brainteasers de- fying commonsense,.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection NIMZ at SemEval-2024 task 9: Evaluating methods in solving brainteasers de- fying commonsense,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:30:29.873356Z

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 e06e9334-58b6-4663-891b-3b640c3fbf88 · outbound

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

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Sharif-STR at SemEval-2024 Task 1: Transformer as a Regression Model for Fine-Grained Scoring of Textual Semantic Relations

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T14:30:29.440759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6bad4ba-3943-4929-a180-c8e0c86a8515 · outbound

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

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T14:30:29.446038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 221fd680-a00d-44c8-832c-604c11fbe910 · outbound

This paper cites Do Adversarially Robust ImageNet Models Transfer Better?.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Do Adversarially Robust ImageNet Models Transfer Better?

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-10T14:30:29.450912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4833102f-2b55-47c6-9579-e50872059f75 · outbound

This paper cites Reliable eval- uation of adversarial robustness with an ensemble of diverse parameter-free attacks,.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Reliable eval- uation of adversarial robustness with an ensemble of diverse parameter-free attacks,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:30:29.857137Z

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 e6f7bed6-b326-49b0-837e-9573870a7b09 · outbound

This paper cites Head ct - hemorrhage,.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Head ct - hemorrhage,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:30:29.841922Z

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 8d1599a8-6a94-4f9e-b00e-4647032ef809 · outbound

This paper cites Brain tumor clas- sification (mri),.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Brain tumor clas- sification (mri),

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:30:29.826762Z

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 671afdb8-93bc-44d6-a969-8751b3aafe1f · outbound

This paper cites Covid-19 image data collection,.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Covid-19 image data collection,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:30:29.810611Z

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 4d6e2143-a9ea-4d4c-9c06-893888607d6e · outbound

This paper cites Brain tumor mri dataset,.

Killing it with Zero-Shot: Adversarially Robust Novelty Detection Brain tumor mri dataset,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:30:29.795375Z

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.

Pith citing papers

Observation abdd564d-bbad-441a-8179-11e8bc7eb296 · inbound