Paper Citation Record · LEDGER

SyncMapV2: Robust and Adaptive Unsupervised Segmentation

As of 20 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2506.16297.

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

pith.paper-citation-record.v1
2506.16297 v3

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:51:05.018037Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

43 of 43 outbound references displayed

verified exact0
verified fuzzy31
unresolved12
parse uncertain0
malformed identifier0
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External citation measurements

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Outbound references

Observation 7acbe35d-ba62-45bd-a396-e9607bcb6747 · outbound

This paper cites Why do deep convolutional networks generalize so poorly to small image transformations? Journal of Machine Learning Research, 20(184):1–25, 2019.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Why do deep convolutional networks generalize so poorly to small image transformations? Journal of Machine Learning Research, 20(184):1–25, 2019

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.871495Z

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Observation 0c0f357e-972f-419c-a73f-0f57b7b8df68 · outbound

This paper cites Do CIFAR-10 Classifiers Generalize to CIFAR-10?.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Do CIFAR-10 Classifiers Generalize to CIFAR-10?

Reference 2

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Observation 23c95516-3f85-4284-a80d-0b8a86b860a6 · outbound

This paper cites Intriguing properties of neural networks.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Intriguing properties of neural networks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.854193Z

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Observation f73d6cdb-474b-45fc-a5ef-dea1f7dc4e47 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Explaining and Harnessing Adversarial Examples

Reference 4

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Observation a5de8276-b247-4feb-966d-55802467e5b1 · outbound

This paper cites Deep neural networks are easily fooled: High confidence predictions for unrecognizable images.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Deep neural networks are easily fooled: High confidence predictions for unrecognizable images

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.835814Z

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Observation 74687e42-942d-44d4-aaf9-dbf7df7c013e · outbound

This paper cites Adversarial robustness assessment: Why in evaluation both L0 and L∞ attacks are necessary.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Adversarial robustness assessment: Why in evaluation both L0 and L∞ attacks are necessary

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.819687Z

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Observation 92f01872-d175-4cf5-b10f-1658c2c74bad · outbound

This paper cites Thermometer encoding: One hot way to resist adversarial examples.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Thermometer encoding: One hot way to resist adversarial examples

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.802401Z

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Observation be2ff2d4-ae86-46eb-9090-f01274fdf0e8 · outbound

This paper cites Adversarial risk and the dangers of evaluating against weak attacks.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Adversarial risk and the dangers of evaluating against weak attacks

Reference 8

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Observation b0e48641-00a6-4319-8845-4cdc7483e250 · outbound

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

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 9

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Observation 028f5258-0ba6-4f5b-8c41-6bf3ee0d64aa · outbound

This paper cites Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks

Reference 10

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Observation d0d05632-a983-436b-9924-53a1e905544b · outbound

This paper cites Adversarial examples are not easily detected: Bypassing ten detection methods.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Adversarial examples are not easily detected: Bypassing ten detection methods

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.770961Z

Source-reported events for the cited work

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Observation 24561953-333c-4f6d-bd5b-12a94764962a · outbound

This paper cites Continual general chunking problem and syncmap.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Continual general chunking problem and syncmap

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.754568Z

Source-reported events for the cited work

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Observation b96ee7dd-8b5b-4ab1-8526-d7f1ad1cf056 · outbound

This paper cites Unsupervised universal image segmentation.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Unsupervised universal image segmentation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.739160Z

Source-reported events for the cited work

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Observation fd1b8b54-9614-40da-b040-47d36cf85f62 · outbound

This paper cites Unsupervised Semantic Segmentation by Distilling Feature Correspondences.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Unsupervised Semantic Segmentation by Distilling Feature Correspondences

Reference 14

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Observation dd3971c2-f36c-485d-a6f5-70d3494c533c · outbound

This paper cites Comaniciu and P.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Comaniciu and P

Reference 15

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Source-reported events for the cited work

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Observation 1bdd33b2-d3f1-4502-bf20-b505f18df440 · outbound

This paper cites Efficient graph-based image segmentation.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Efficient graph-based image segmentation

Reference 16

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Observation 0361cb6b-b2a3-40a7-80e2-a45732bc3bc1 · outbound

This paper cites W-Net: A Deep Model for Fully Unsupervised Image Segmentation.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation W-Net: A Deep Model for Fully Unsupervised Image Segmentation

Reference 17

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Source-reported events for the cited work

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Observation 1393cd05-ccc2-4367-ae97-f2029519505c · outbound

This paper cites Unsupervised image segmentation by backpropagation.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Unsupervised image segmentation by backpropagation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.699907Z

Source-reported events for the cited work

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Observation a137f1ce-1036-41dd-ac85-6f37a0a2723b · outbound

This paper cites Invariant information clustering for unsupervised image classification and segmentation.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Invariant information clustering for unsupervised image classification and segmentation

Reference 19

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Observation 048d0345-4137-4111-bc9f-b394c8ce103c · outbound

This paper cites Unsupervised learning of image segmentation based on differentiable feature clustering.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Unsupervised learning of image segmentation based on differentiable feature clustering

Reference 20

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Observation b20f6688-cfd7-47cf-9f5e-846609a3706e · outbound

This paper cites Pixel-level clustering network for unsupervised image segmentation.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Pixel-level clustering network for unsupervised image segmentation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.647203Z

Source-reported events for the cited work

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Observation 083c7da4-d790-4a50-9d60-49af1dd433df · outbound

This paper cites Two views on the cognitive brain.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Two views on the cognitive brain

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.628543Z

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Observation 3575b037-b4d7-4cb2-8ca7-8ab220614b28 · outbound

This paper cites Accurate estimation of neural population dynamics without spike sorting.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Accurate estimation of neural population dynamics without spike sorting

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.612054Z

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Observation bf4894b3-b7c1-4e8c-a826-d86d672b0227 · outbound

This paper cites The importance of mixed selectivity in complex cognitive tasks.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation The importance of mixed selectivity in complex cognitive tasks

Reference 24

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Observation 0a903ecb-ded0-4bec-a115-964a6038144f · outbound

This paper cites A survey on reservoir computing and its interdisciplinary applica- tions beyond traditional machine learning.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation A survey on reservoir computing and its interdisciplinary applica- tions beyond traditional machine learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.584303Z

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Observation eb577673-6dfa-49b4-b56b-08d03644ae4b · outbound

This paper cites Smooseg: smoothness prior for unsupervised semantic segmentation.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Smooseg: smoothness prior for unsupervised semantic segmentation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.566794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6b53f1fb-5931-48ad-9ef3-849204c50888 · outbound

This paper cites Diffuse attend and segment: Unsupervised zero-shot segmentation using stable diffusion.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Diffuse attend and segment: Unsupervised zero-shot segmentation using stable diffusion

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.550127Z

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Observation e68a8a73-1084-4cf9-8435-8412e9ff0192 · outbound

This paper cites Unsupervised semantic segmentation through depth-guided feature correlation and sampling.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Unsupervised semantic segmentation through depth-guided feature correlation and sampling

Reference 28

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Source-reported events for the cited work

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Observation 5d8da29f-60a8-4fd3-bcaf-45e3e6b9e3d1 · outbound

This paper cites Dbscan revisited, revisited: why and how you should (still) use dbscan.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Dbscan revisited, revisited: why and how you should (still) use dbscan

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.511974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f8c7ccd2-9687-4ec3-a637-2c1f0f5ada06 · outbound

This paper cites Algorithms for hierarchical clustering: an overview.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Algorithms for hierarchical clustering: an overview

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.493563Z

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Observation 637fa7e9-8ee2-4f18-a4a2-83175f93f741 · outbound

This paper cites Symmetrical syncmap for imbalanced general chunking problems.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Symmetrical syncmap for imbalanced general chunking problems

Reference 31

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Observation 1a41351e-8f6f-4c1e-a490-02fdafc1b6c0 · outbound

This paper cites Gestalt psychology.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Gestalt psychology

Reference 32

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Observation 05fdf92a-3362-4b35-abd4-7accc0ab4329 · outbound

This paper cites Principles of neural science, volume 4.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Principles of neural science, volume 4

Reference 33

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Observation 3d42fa3e-a226-426a-af8d-86d4bbf93c27 · outbound

This paper cites echo state.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation echo state

Reference 34

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Observation 9c7a8957-fbdd-4889-b78b-39dc7c5d4a29 · outbound

This paper cites Computing and visualizing dynamic time warping alignments in r: the dtw package.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Computing and visualizing dynamic time warping alignments in r: the dtw package

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.425315Z

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Observation a5552702-cd72-4aaf-8b1f-bf563d0884ae · outbound

This paper cites The pascal visual object classes challenge: A retrospective.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation The pascal visual object classes challenge: A retrospective

Reference 36

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Observation 942a011c-7fc4-4e54-a42a-863892ea861d · outbound

This paper cites Contour detection and hierarchical image segmentation.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Contour detection and hierarchical image segmentation

Reference 37

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Observation c8374169-b1c6-4d45-8b49-2bbd399d8457 · outbound

This paper cites Dic: deep image clustering for unsupervised image segmentation.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Dic: deep image clustering for unsupervised image segmentation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.375521Z

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Observation d03a7fcd-af36-4263-a026-26471f41bf9d · outbound

This paper cites Segmentation using superpixels: A bipartite graph partitioning approach.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Segmentation using superpixels: A bipartite graph partitioning approach

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.360473Z

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Observation eab5a732-409e-425a-93cf-bbec2d6c360d · outbound

This paper cites Benchmarking neural network robustness to common corruptions and perturbations.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Benchmarking neural network robustness to common corruptions and perturbations

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.344449Z

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Observation 3740664c-282e-4220-9be1-5cadde9c57a3 · outbound

This paper cites Dynaseg: A deep dynamic fusion method for unsuper- vised image segmentation incorporating feature similarity and spatial continuity.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Dynaseg: A deep dynamic fusion method for unsuper- vised image segmentation incorporating feature similarity and spatial continuity

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.328022Z

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Observation f9a90208-64e5-4cda-9e03-a5c2e9a771cf · outbound

This paper cites A practical guide to applying echo state networks.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation A practical guide to applying echo state networks

Reference 42

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Observation 0bc84008-eb22-434a-9d88-f4d290712d84 · outbound

This paper cites Reservoir computing approaches to recurrent neural network training.

SyncMapV2: Robust and Adaptive Unsupervised Segmentation Reservoir computing approaches to recurrent neural network training

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.296062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.