Paper Citation Record · LEDGER

Autoencoders for Anomaly Detection are Unreliable

As of 19 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2501.13864.

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

pith.paper-citation-record.v1
2501.13864 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T15:37:16.776857Z

measured 43 of 43 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 2026-07-08T16:50:19.900257Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-08T16:55:08.419571Z

Reference resolution

42 of 42 outbound references displayed

verified exact0
verified fuzzy31
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 459f2145-904e-4427-b779-b6bc8982141b · outbound

This paper cites write newline.

Autoencoders for Anomaly Detection are Unreliable write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T15:37:16.612532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 330517a6-3f89-46f7-961f-974265ca9cfa · outbound

This paper cites Learning Not to Reconstruct Anomalies.

Autoencoders for Anomaly Detection are Unreliable Learning Not to Reconstruct Anomalies

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T15:37:16.617557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73643fa4-4e33-402c-8419-ac2e25fc360f · outbound

This paper cites Exploiting autoencoder’s weakness to generate pseudo anomalies.

Autoencoders for Anomaly Detection are Unreliable Exploiting autoencoder’s weakness to generate pseudo anomalies

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:17.267225Z

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.

Observation 0923e08b-fe5d-4e90-83ed-c1eb2322f7bc · outbound

This paper cites Neural networks and principal component analysis: Learning from examples without local minima.

Autoencoders for Anomaly Detection are Unreliable Neural networks and principal component analysis: Learning from examples without local minima

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:17.256304Z

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.

Observation 057b8a25-59f1-42ec-95c1-67d30408f57d · outbound

This paper cites Computer vision and deep learning--based data anomaly detection method for structural health monitoring.

Autoencoders for Anomaly Detection are Unreliable Computer vision and deep learning--based data anomaly detection method for structural health monitoring

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:17.244788Z

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.

Observation a0546b64-a6fa-4fee-8bc4-f3a2686ec4ef · outbound

This paper cites Robust anomaly detection in images using adversarial autoencoders.

Autoencoders for Anomaly Detection are Unreliable Robust anomaly detection in images using adversarial autoencoders

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:17.233519Z

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.

Observation f26febaf-f62e-4862-b870-0b6fb5246678 · outbound

This paper cites What do aes learn? challenging common assumptions in unsupervised anomaly detection.

Autoencoders for Anomaly Detection are Unreliable What do aes learn? challenging common assumptions in unsupervised anomaly detection

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:17.221518Z

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.

Observation 5b23b2fc-95e6-4ef9-a142-207654a09a47 · outbound

This paper cites The MVTec anomaly detection dataset: a comprehensive real-world dataset for unsupervised anomaly detection.

Autoencoders for Anomaly Detection are Unreliable The MVTec anomaly detection dataset: a comprehensive real-world dataset for unsupervised anomaly detection

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:17.209678Z

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.

Observation c47c87bc-344f-4163-8e67-c1cdeb48ae4f · outbound

This paper cites Unsupervised anomaly detection algorithms on real-world data: How many do we need? Journal of Machine Learning Research, 25 0 (105): 0 1--34, 2024.

Autoencoders for Anomaly Detection are Unreliable Unsupervised anomaly detection algorithms on real-world data: How many do we need? Journal of Machine Learning Research, 25 0 (105): 0 1--34, 2024

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:17.198596Z

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.

Observation 1538ad90-30e5-410d-bcc5-fb2da1f68c44 · outbound

This paper cites Auto-association by multilayer perceptrons and singular value decomposition.

Autoencoders for Anomaly Detection are Unreliable Auto-association by multilayer perceptrons and singular value decomposition

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:17.187134Z

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.

Observation 05f6f828-4e7b-437a-b3f3-6462313bceac · outbound

This paper cites Rethinking autoencoders for medical anomaly detection from a theoretical perspective.

Autoencoders for Anomaly Detection are Unreliable Rethinking autoencoders for medical anomaly detection from a theoretical perspective

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:17.175704Z

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.

Observation ecc08f31-b0cd-47b4-985e-2bcc481c0c16 · outbound

This paper cites Improved autoencoder for unsupervised anomaly detection.

Autoencoders for Anomaly Detection are Unreliable Improved autoencoder for unsupervised anomaly detection

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:17.164722Z

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.

Observation b8bd9c96-0433-466f-a63c-6d19731c85d5 · outbound

This paper cites Anomaly detection of defects on concrete structures with the convolutional autoencoder.

Autoencoders for Anomaly Detection are Unreliable Anomaly detection of defects on concrete structures with the convolutional autoencoder

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T15:37:16.663204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8733a2ca-fc6c-43ef-a129-7710377d8fc9 · outbound

This paper cites An examination on autoencoder designs for anomaly detection in video surveillance.

Autoencoders for Anomaly Detection are Unreliable An examination on autoencoder designs for anomaly detection in video surveillance

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:17.145499Z

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.

Observation 6f2fe857-c270-4710-bea8-9a7735e8375c · outbound

This paper cites Improving Reconstruction Autoencoder Out-of-distribution Detection with Mahalanobis Distance.

Autoencoders for Anomaly Detection are Unreliable Improving Reconstruction Autoencoder Out-of-distribution Detection with Mahalanobis Distance

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T15:37:16.670128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6188d6d-6bd0-41a4-9aaa-73d4c3629b6f · outbound

This paper cites A deep auto-encoder based approach for intrusion detection system.

Autoencoders for Anomaly Detection are Unreliable A deep auto-encoder based approach for intrusion detection system

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:17.131900Z

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.

Observation 62eeaff5-2613-46d3-9001-43fc21eafbf4 · outbound

This paper cites Deep sparse rectifier neural networks.

Autoencoders for Anomaly Detection are Unreliable Deep sparse rectifier neural networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:17.119348Z

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.

Observation 46afd4b8-648a-4ee6-a3e0-0002ffb36243 · outbound

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

Autoencoders for Anomaly Detection are Unreliable Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:17.107989Z

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.

Observation 6d188913-c9d5-4ca1-8473-e05d64dcfd3c · outbound

This paper cites Hierarchical vaes know what they don’t know.

Autoencoders for Anomaly Detection are Unreliable Hierarchical vaes know what they don’t know

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:17.096686Z

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.

Observation dde91417-4b1e-4974-b3a5-3fa7ca798f3e · outbound

This paper cites Untersuchungen zu dynamischen neuronalen netzen.

Autoencoders for Anomaly Detection are Unreliable Untersuchungen zu dynamischen neuronalen netzen

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:17.085365Z

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.

Observation 382bf5a2-9efa-4a55-b5b7-7012980e3bfa · outbound

This paper cites Anomaly detection for predictive maintenance in industry 4.0-a survey.

Autoencoders for Anomaly Detection are Unreliable Anomaly detection for predictive maintenance in industry 4.0-a survey

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:17.073808Z

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.

Observation ceaf2887-3555-457e-a35c-acbab35306d4 · outbound

This paper cites The mnist database of handwritten digits.

Autoencoders for Anomaly Detection are Unreliable The mnist database of handwritten digits

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T15:37:16.701484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bea33fad-e489-471c-8c04-707a1d8c1551 · outbound

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

Autoencoders for Anomaly Detection are Unreliable Hierarchical vector quantized transformer for multi-class unsupervised anomaly detection

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:17.055153Z

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.

Observation b2cd2782-5057-474c-aa10-13ddf84c6e6a · outbound

This paper cites Outlier detection using autoencoders.

Autoencoders for Anomaly Detection are Unreliable Outlier detection using autoencoders

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:17.042714Z

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.

Observation 9b38c480-83a3-4fe0-84d6-b36cb9261a6e · outbound

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

Autoencoders for Anomaly Detection are Unreliable Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T15:37:16.712609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3cb5fe14-3dd6-4b69-a173-224f1b7dd817 · outbound

This paper cites Modified autoencoder training and scoring for robust unsupervised anomaly detection in deep learning.

Autoencoders for Anomaly Detection are Unreliable Modified autoencoder training and scoring for robust unsupervised anomaly detection in deep learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:17.031590Z

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.

Observation c9290286-9d2e-4be3-b58b-1dfa7f12de58 · outbound

This paper cites Do deep generative models know what they don't know? In International Conference on Learning Representations, 2019.

Autoencoders for Anomaly Detection are Unreliable Do deep generative models know what they don't know? In International Conference on Learning Representations, 2019

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:17.017131Z

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.

Observation cd224e61-a79c-425e-a3c4-c7d2172bbd6f · outbound

This paper cites A comprehensive review on deep learning-based methods for video anomaly detection.

Autoencoders for Anomaly Detection are Unreliable A comprehensive review on deep learning-based methods for video anomaly detection

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:17.005063Z

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.

Observation f8bd33c1-e977-47d9-b746-99a0d0b61f70 · outbound

This paper cites Network anomaly detection using lstm based autoencoder.

Autoencoders for Anomaly Detection are Unreliable Network anomaly detection using lstm based autoencoder

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:16.993086Z

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.

Observation 149a6ee2-c66a-4177-84f4-9294c42e945a · outbound

This paper cites Arae: Adversarially robust training of autoencoders improves novelty detection.

Autoencoders for Anomaly Detection are Unreliable Arae: Adversarially robust training of autoencoders improves novelty detection

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:16.981950Z

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.

Observation 56dee937-8c1c-4714-9c43-2c6a47ba2e29 · outbound

This paper cites Real-world anomaly detection in surveillance videos.

Autoencoders for Anomaly Detection are Unreliable Real-world anomaly detection in surveillance videos

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:16.970736Z

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.

Observation 591fb71f-133a-4cc0-b00d-b23191878cfa · outbound

This paper cites Fixing bias in reconstruction-based anomaly detection with lipschitz discriminators.

Autoencoders for Anomaly Detection are Unreliable Fixing bias in reconstruction-based anomaly detection with lipschitz discriminators

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:16.959138Z

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.

Observation 7b977e16-f1c0-441a-8369-6d3962c3d782 · outbound

This paper cites Autoencoder-based anomaly detection for surface defect inspection.

Autoencoders for Anomaly Detection are Unreliable Autoencoder-based anomaly detection for surface defect inspection

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:16.946651Z

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.

Observation b2df2300-f510-445f-bdb9-ff3a25aa7595 · outbound

This paper cites Anomaly detection for medical images based on a one-class classification.

Autoencoders for Anomaly Detection are Unreliable Anomaly detection for medical images based on a one-class classification

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:16.933154Z

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.

Observation 0add2f0b-ab6d-486c-81c2-f2c607949878 · outbound

This paper cites Autoencoding under normalization constraints.

Autoencoders for Anomaly Detection are Unreliable Autoencoding under normalization constraints

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:16.920959Z

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.

Observation d6c3be37-5935-43b9-be93-2492a849dfc5 · outbound

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

Autoencoders for Anomaly Detection are Unreliable A unified model for multi-class anomaly detection

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:16.908547Z

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.

Observation 60fccd1c-467f-4a63-b1b9-2caa18ea3de1 · outbound

This paper cites Spatio-temporal autoencoder for video anomaly detection.

Autoencoders for Anomaly Detection are Unreliable Spatio-temporal autoencoder for video anomaly detection

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:37:16.887546Z

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.

Observation 50bd8a18-92ae-41fd-92a2-455066129c15 · outbound

This paper cites Rethinking reconstruction autoencoder-based out-of-distribution detection.

Autoencoders for Anomaly Detection are Unreliable Rethinking reconstruction autoencoder-based out-of-distribution detection

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-10T15:37:16.761432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df6bb90e-e2ec-4a80-924d-215cb3bb26e5 · outbound

This paper cites Deep autoencoding gaussian mixture model for unsupervised anomaly detection.

Autoencoders for Anomaly Detection are Unreliable Deep autoencoding gaussian mixture model for unsupervised anomaly detection

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-10T15:37:16.765024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4576fb93-828c-4fce-90f5-61f7ac5920c5 · outbound

This paper cites @esa (Ref.

Autoencoders for Anomaly Detection are Unreliable @esa (Ref

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-10T15:37:16.768778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a182ef6a-7ff3-4ff8-be4d-e69dfc88005d · outbound

This paper cites an unresolved cited work.

Autoencoders for Anomaly Detection are Unreliable Unresolved cited work

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-10T15:37:16.772916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e39cf48-feef-4091-a9cc-32e682cb89b3 · outbound

This paper cites an unresolved cited work.

Autoencoders for Anomaly Detection are Unreliable Unresolved cited work

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-10T15:37:16.776857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation e60c1ab8-8968-49a5-b392-c50bc20c7e06 · inbound

Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems cites this paper.

Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems Autoencoders for Anomaly Detection are Unreliable

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-07-08T16:55:08.421128Z

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.