Paper Citation Record · LEDGER

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection

As of 17 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2506.13097.

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

pith.paper-citation-record.v1
2506.13097 v3

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:09:17.334097Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

28 of 28 outbound references displayed

verified exact0
verified fuzzy21
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4a60f785-d3c7-4c2e-bbef-4fde3f54da58 · outbound

This paper cites Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:17.908758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 06fbebec-c72c-49f2-917e-82bc7077cb66 · outbound

This paper cites Beyond dents and scratches: Logical constraints in unsupervised anomaly detection and localization.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection Beyond dents and scratches: Logical constraints in unsupervised anomaly detection and localization

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:17.890883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 274e6d04-fa85-40a8-8625-796126cf0877 · outbound

This paper cites MVTec AD–A comprehensive real-world dataset for unsupervised anomaly detection.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection MVTec AD–A comprehensive real-world dataset for unsupervised anomaly detection

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:17.868993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 58716fa6-1efd-4419-bea7-bd195a780a01 · outbound

This paper cites Vision Transformers Need Registers.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection Vision Transformers Need Registers

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:17.202447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c233c67d-f728-4ac9-ba79-bbd3c8ec06a0 · outbound

This paper cites Padim: a patch distribution modeling framework for anomaly detection and localization.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection Padim: a patch distribution modeling framework for anomaly detection and localization

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:17.847105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e42c4bf0-f2cd-46fe-8960-51161bc56c5d · outbound

This paper cites Anomaly detection via reverse distillation from one-class embedding.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection Anomaly detection via reverse distillation from one-class embedding

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:17.826014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 83f41654-5741-4cf9-b3cf-6a14224c6708 · outbound

This paper cites Memorizing normality to detect anomaly: Memory-augmented deep autoen- coder for unsupervised anomaly detection.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection Memorizing normality to detect anomaly: Memory-augmented deep autoen- coder for unsupervised anomaly detection

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:17.807754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e0e583b8-7e8b-4599-96f4-bcc8d7e5779f · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:17.225449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c2e710b-0088-4287-8f26-c23472d376fd · outbound

This paper cites Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:17.230935Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4fbbd916-95c1-4ead-ad97-98ece2a97c9a · outbound

This paper cites A diffusion-based framework for multi-class anomaly detection.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection A diffusion-based framework for multi-class anomaly detection

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:17.787929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3239410d-b08d-4134-a824-3cc38cb16f6d · outbound

This paper cites MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:17.241748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 774b6716-08e1-4467-b0f9-20df5b38f233 · outbound

This paper cites Pixel-level anomaly detection via uncertainty-aware prototypical trans- former.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection Pixel-level anomaly detection via uncertainty-aware prototypical trans- former

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:17.768464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fa0b94b9-4bc0-4579-ae84-417916194ce3 · outbound

This paper cites Adaptive prototype learning and allocation for few-shot segmentation.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection Adaptive prototype learning and allocation for few-shot segmentation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:17.747037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2bec9d9d-d14b-4900-919a-3681b18c1e1f · outbound

This paper cites Focal loss for dense object detection.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection Focal loss for dense object detection

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:17.258282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e00a1f7c-8a38-479a-9804-8490e9037af1 · outbound

This paper cites Simplenet: A simple network for image anomaly detection and localization.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection Simplenet: A simple network for image anomaly detection and localization

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:17.714789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e1d6578d-8edd-416e-bc11-ea5d32d516ec · outbound

This paper cites Hierarchical vector quantized transformer for multi-class unsupervised anomaly detection.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection Hierarchical vector quantized transformer for multi-class unsupervised anomaly detection

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:17.697691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 205b951b-a1f0-4357-a23a-853298185c3d · outbound

This paper cites Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:17.274885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fc66e3e-e8b8-4080-81a0-66d580d327d1 · outbound

This paper cites Learning normal dynamics in videos with meta prototype network.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection Learning normal dynamics in videos with meta prototype network

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:17.678677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c8fc019e-66a2-4638-a1f6-d25c4e5c2a17 · outbound

This paper cites Learning memory-guided normality for anomaly detection.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection Learning memory-guided normality for anomaly detection

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:17.659081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 26fde2c0-cfdc-4d2c-af30-74401ac111d0 · outbound

This paper cites Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:17.640171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ceccf64f-0a92-4147-954a-388bc155e3d3 · outbound

This paper cites Towards total recall in industrial anomaly detection.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection Towards total recall in industrial anomaly detection

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:17.622495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9b75bbe4-69b1-4caa-b0f5-1d5af40ec7a4 · outbound

This paper cites Prototypical networks for few-shot learning.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection Prototypical networks for few-shot learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:17.597971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a4a8912e-02c9-42fb-8710-f1ec7e6b8ee5 · outbound

This paper cites Real-iad: A real-world multi-view dataset for benchmarking versatile industrial anomaly detection.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection Real-iad: A real-world multi-view dataset for benchmarking versatile industrial anomaly detection

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:17.576310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9eba4af7-aa39-4248-ab04-5dca5da2b7a6 · outbound

This paper cites A unified model for multi-class anomaly detection.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection A unified model for multi-class anomaly detection

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:17.556387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c6468dc9-2382-44e6-a649-9eb3593032e1 · outbound

This paper cites Wide Residual Networks.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection Wide Residual Networks

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:17.318252Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 980f26ec-5923-4a7b-9c86-9dd3109049f4 · outbound

This paper cites Destseg: Segmentation guided denoising student-teacher for anomaly detection.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection Destseg: Segmentation guided denoising student-teacher for anomaly detection

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:17.530809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a84af798-611b-44ab-a348-1bcdf9cc8b92 · outbound

This paper cites Omnial: A unified cnn framework for unsupervised anomaly localization.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection Omnial: A unified cnn framework for unsupervised anomaly localization

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:17.513049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 617ced0f-6485-4f96-bb16-af5bef8905d6 · outbound

This paper cites Spot-the-difference self-supervised pre-training for anomaly detection and segmentation.

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection Spot-the-difference self-supervised pre-training for anomaly detection and segmentation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:17.495482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.