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Paper Citation Record · LEDGER

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations

As of 10 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2511.09749.

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

pith.paper-citation-record.v1
2511.09749 v2

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-17T23:00:05.791459Z

measured 34 of 34 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

34 of 34 outbound references displayed

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  • verified fuzzy29
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5db68c64-46a7-4059-9cc3-72edafbbd9f5 · outbound

This paper cites gov/IREX10/.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations gov/IREX10/

Reference 1

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Observation 8a02137e-ac38-4f92-8155-0da82be8fbde · outbound

This paper cites Accessed: Sept.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations Accessed: Sept

Reference 2

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Source-reported events for the cited work

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Observation cd9c1eb7-afea-4cc3-a552-c336cd33d743 · outbound

This paper cites com / CVRL / OpenSourceIrisRecognition/.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations com / CVRL / OpenSourceIrisRecognition/

Reference 3

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Observation 95c0e17f-eebf-46e3-aa8c-41be119b678c · outbound

This paper cites Comprehensive study in open- set iris presentation attack detection.IEEE Transactions on Information Forensics and Security, 18:3238–3250.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations Comprehensive study in open- set iris presentation attack detection.IEEE Transactions on Information Forensics and Security, 18:3238–3250

Reference 4

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Source-reported events for the cited work

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Observation c7f67710-fcbf-4618-88e2-6835619271f8 · outbound

This paper cites An iris image synthesis method based on pca and super-resolution.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations An iris image synthesis method based on pca and super-resolution

Reference 5

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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.

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Observation ed245538-b90e-4ea0-a43b-9dea89010361 · outbound

This paper cites Domain-specific human-inspired binarized statisti- cal image features for iris recognition.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations Domain-specific human-inspired binarized statisti- cal image features for iris recognition

Reference 6

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Source-reported events for the cited work

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Observation 9e34d456-0d3f-4ae0-832d-580a474986e8 · outbound

This paper cites High confidence visual recognition of per- sons by a test of statistical independence.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations High confidence visual recognition of per- sons by a test of statistical independence

Reference 7

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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.

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Observation c0a4c1b7-5f44-47ac-a026-261db91faafc · outbound

This paper cites Improved training of wasserstein gans.Advances in neural information processing systems, 30.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations Improved training of wasserstein gans.Advances in neural information processing systems, 30

Reference 8

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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.

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Observation 43869821-9f3d-4a16-af6b-e2a4560187f8 · outbound

This paper cites Information technology — Biometric data interchange formats — Part 6: Iris image data.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations Information technology — Biometric data interchange formats — Part 6: Iris image data

Reference 9

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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.

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Observation 235ea981-7fb0-4ca7-abad-e794446baeae · outbound

This paper cites Alias-free generative adversarial networks.Advances in neural infor- mation processing systems, 34:852–863.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations Alias-free generative adversarial networks.Advances in neural infor- mation processing systems, 34:852–863

Reference 10

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Source-reported events for the cited work

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Observation a56ec1d1-c855-4114-b4e6-867c29ce6662 · outbound

This paper cites De- formirisnet: An identity-preserving model of iris texture de- formation.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations De- formirisnet: An identity-preserving model of iris texture de- formation

Reference 11

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verified fuzzy
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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.

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Observation b14ee851-ce58-4414-ac8e-f90f0564d76e · outbound

This paper cites Synthetic iris presentation attack using idcgan.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations Synthetic iris presentation attack using idcgan

Reference 12

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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.

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Observation 983a1a77-9e98-4c9a-9178-9f1f6b808ef4 · outbound

This paper cites Conditional generative adversarial network-based data aug- mentation for enhancement of iris recognition accuracy.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations Conditional generative adversarial network-based data aug- mentation for enhancement of iris recognition accuracy

Reference 13

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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.

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Observation 8e333469-5425-4f30-9ee2-96fcb32c3773 · outbound

This paper cites Conditional wasserstein generative adversarial networks for rebalancing iris image datasets.IEICE TRANSACTIONS on Information and Sys- tems, 104(9):1450–1458.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations Conditional wasserstein generative adversarial networks for rebalancing iris image datasets.IEICE TRANSACTIONS on Information and Sys- tems, 104(9):1450–1458

Reference 14

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verified fuzzy
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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.

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Observation 89e6a9a9-4f98-4b31-befd-1e8a389012b0 · outbound

This paper cites Synthesis of iris images using markov random fields.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations Synthesis of iris images using markov random fields

Reference 15

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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.

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Observation a50a1290-060f-4bf8-8cd1-93fe65ab9587 · outbound

This paper cites Iris-GAN: Learning to Generate Realistic Iris Images Using Convolutional GAN.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations Iris-GAN: Learning to Generate Realistic Iris Images Using Convolutional GAN

Reference 16

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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.

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Observation 9ff5df6f-fbd6-47a3-9a45-7d444a4a243a · outbound

This paper cites RSGAN: Face Swapping and Editing using Face and Hair Representation in Latent Spaces.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations RSGAN: Face Swapping and Editing using Face and Hair Representation in Latent Spaces

Reference 17

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verified exact
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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.

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Observation 88c8a896-8472-4696-9a58-8d0c5ca4df8f · outbound

This paper cites OSIRIS: An open source iris recognition software.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations OSIRIS: An open source iris recognition software

Reference 18

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verified fuzzy
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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.

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Observation 1e0ef217-f4e3-4044-8199-5b156c60d903 · outbound

This paper cites Synthetic Iris Image Databases and Identity Leakage: Risks and Mitigation Strategies.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations Synthetic Iris Image Databases and Identity Leakage: Risks and Mitigation Strategies

Reference 19

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verified exact
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Source-reported events for the cited work

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Observation 457ac589-e929-4797-b160-6803371aa0c4 · outbound

This paper cites Generating synthetic irises by feature agglomeration.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations Generating synthetic irises by feature agglomeration

Reference 20

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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.

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Observation 639dc517-4094-432e-b603-40de4bddd9aa · outbound

This paper cites In- terpreting the latent space of gans for semantic face editing.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations In- terpreting the latent space of gans for semantic face editing

Reference 21

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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.

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Observation 07ecff78-63ac-42d3-938f-51e80d11b41c · outbound

This paper cites Latent Traversals in Generative Models as Potential Flows.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations Latent Traversals in Generative Models as Potential Flows

Reference 22

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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.

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Observation be3fe7e4-055a-445e-b232-404bb554545f · outbound

This paper cites Warpedganspace: Finding non-linear rbf paths in gan latent space.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations Warpedganspace: Finding non-linear rbf paths in gan latent space

Reference 23

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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.

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Observation 5ff2159d-4385-4325-b84c-40cea7428f4a · outbound

This paper cites Generating intra-and inter-class iris images by identity con- trast.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations Generating intra-and inter-class iris images by identity con- trast

Reference 24

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raw_fallback, observed 2026-05-17T23:00:25.971587Z

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.

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Observation d9e57ccf-3395-40ea-bd8e-7ee4574f2041 · outbound

This paper cites Iris synthesis: a reverse subdivision application.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations Iris synthesis: a reverse subdivision application

Reference 25

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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.

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Observation f65c6c16-a09f-4457-87e0-80217cb683b7 · outbound

This paper cites A multiresolution approach to iris synthesis.Computers & Graphics, 34(4):468–478.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations A multiresolution approach to iris synthesis.Computers & Graphics, 34(4):468–478

Reference 26

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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.

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Observation 15184240-25db-4f0d-bb90-9432c72f6466 · outbound

This paper cites Synthesis of large realistic iris databases using patch-based sampling.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations Synthesis of large realistic iris databases using patch-based sampling

Reference 27

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verified fuzzy
raw_fallback, observed 2026-05-17T23:00:25.969398Z

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.

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Observation 2c62c799-4ac1-484f-a95e-d5e4c0f7a5b8 · outbound

This paper cites Gan inversion: A survey.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations Gan inversion: A survey

Reference 28

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raw_fallback, observed 2026-05-17T23:00:25.973467Z

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.

source=pdf_text observed=2026-05-17T23:00:05.791459Z digest=sha256:5fef4891623036694fb3e87a519fde746ca18ae7a442dd18f8d102e37aa6d00d

Observation 04cfb942-ccc5-4d1d-898a-4343572c656b · outbound

This paper cites Synthesiz- ing iris images using rasgan with application in presentation attack detection.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations Synthesiz- ing iris images using rasgan with application in presentation attack detection

Reference 29

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raw_fallback, observed 2026-05-17T23:00:25.982515Z

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.

source=pdf_text observed=2026-05-17T23:00:05.791459Z digest=sha256:af904b19598ec835af480b77715442fec0e9e53c15ae5075e5f5205587ea522d

Observation 0f2048df-2c20-49f8-945a-57d5d21a4002 · outbound

This paper cites Cit-gan: Cyclic image translation generative adversarial network with application in iris presentation attack detection.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations Cit-gan: Cyclic image translation generative adversarial network with application in iris presentation attack detection

Reference 30

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verified fuzzy
raw_fallback, observed 2026-05-17T23:00:25.984778Z

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.

source=pdf_text observed=2026-05-17T23:00:05.791459Z digest=sha256:34bffcd4148d81dc85252c00f8d432e1f36bc3e47b8fcf359cbf96abf50f7dd2

Observation 796d0657-5232-40d4-9df5-23bb93b28668 · outbound

This paper cites iwarpgan: Disentangling identity and style to generate synthetic iris images.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations iwarpgan: Disentangling identity and style to generate synthetic iris images

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T23:00:25.986827Z

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.

source=pdf_text observed=2026-05-17T23:00:05.791459Z digest=sha256:779c201bf24e531479a38dccb03128468e099ab9904949784f9a8c9a8fd7dfd1

Observation 34ddf0e2-29b6-421d-b919-417c77568e38 · outbound

This paper cites Synthesizing Iris Images using Generative Adversarial Networks: Survey and Comparative Analysis.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations Synthesizing Iris Images using Generative Adversarial Networks: Survey and Comparative Analysis

Reference 32

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verified exact
arxiv_id, observed 2026-05-17T23:00:25.433236Z

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.

source=pdf_text observed=2026-05-17T23:00:05.791459Z digest=sha256:6ab151d73f6d0c260077c05ff66a7ddd6590db0d7a2a2972b5bd535d9ac7d5e8

Observation 4b62dd27-0d5d-4faf-ae51-86197379f451 · outbound

This paper cites A model based, anatomy based method for synthesizing iris images.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations A model based, anatomy based method for synthesizing iris images

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T23:00:25.993747Z

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.

source=pdf_text observed=2026-05-17T23:00:05.791459Z digest=sha256:6b1874926803614efc2082954db0d321727242ae8906c825cdfd5b4e115aa20e

Observation b668e047-c215-458f-a04d-d02189410679 · outbound

This paper cites On genera- tion and analysis of synthetic iris images.IEEE Transactions on Information Forensics and Security, 2(1):77–90.

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations On genera- tion and analysis of synthetic iris images.IEEE Transactions on Information Forensics and Security, 2(1):77–90

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-05-17T23:00:25.996114Z

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.

source=pdf_text observed=2026-05-17T23:00:05.791459Z digest=sha256:3c970f8cf4f5f37a47c7363a4b82b12cfe4b7e01fef68269e237f590d2ff6626

Pith citing papers

No inbound Pith citation observations are available.