Paper Citation Record · LEDGER

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs

As of 12 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2501.11236.

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

pith.paper-citation-record.v1
2501.11236 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T18:36:29.380992Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

42 of 42 outbound references displayed

verified exact0
verified fuzzy21
unresolved17
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External citation measurements

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Outbound references

Observation 7ae00a81-b90a-40fd-81d2-7829a1b86e51 · outbound

This paper cites Conference on Neural Information Processing Systems 27 (2014).

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs Conference on Neural Information Processing Systems 27 (2014)

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:36:30.028272Z

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Observation 1d99d947-58b9-472c-b36e-745bb641da25 · outbound

This paper cites In: International Conference on Machine Learning, pp.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs In: International Conference on Machine Learning, pp

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:36:30.013596Z

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Observation e0b6a8ea-0526-4ca6-a521-e201821757a6 · outbound

This paper cites Improved Training of Wasserstein GANs.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs Improved Training of Wasserstein GANs

Reference 3

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Observation 154dd4ee-fbc8-4820-aa5f-b9662708798f · outbound

This paper cites In: International Conference on Machine Learning, pp.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs In: International Conference on Machine Learning, pp

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:36:29.999795Z

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Observation 7792bc8c-4caa-4d95-afe6-8060112da9b3 · outbound

This paper cites 3481– 3490 (2018).

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs 3481– 3490 (2018)

Reference 5

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Observation c85c953a-1fe7-4fe6-b925-f9ec5fb38fe9 · outbound

This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence 43(1), 17–32 (2019).

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs IEEE Transactions on Pattern Analysis and Machine Intelligence 43(1), 17–32 (2019)

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:36:29.972059Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation eb144fe8-b2ad-45fa-b048-5cd0b6cb3952 · outbound

This paper cites Stabilizing Training of Generative Adversarial Networks through Regularization.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs Stabilizing Training of Generative Adversarial Networks through Regularization

Reference 7

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Observation 99a25c0e-97a8-47d8-bfc9-636ff916f28e · outbound

This paper cites Diversity-Sensitive Conditional Generative Adversarial Networks.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs Diversity-Sensitive Conditional Generative Adversarial Networks

Reference 8

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Observation 7c0a9fa7-b692-4155-8a18-5f7522416beb · outbound

This paper cites Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Reference 9

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Observation afc5834a-5c26-43ac-a587-06e2ba8d520b · outbound

This paper cites In: International Conference on Machine Learning, pp.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs In: International Conference on Machine Learning, pp

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:36:29.958028Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4e066f68-5047-4c16-a418-a1ab8d7ff643 · outbound

This paper cites Progressive Growing of GANs for Improved Quality, Stability, and Variation.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs Progressive Growing of GANs for Improved Quality, Stability, and Variation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T18:36:29.239557Z

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Observation d68f5edc-f837-4c47-86e8-16ab93911e60 · outbound

This paper cites Large Scale GAN Training for High Fidelity Natural Image Synthesis.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs Large Scale GAN Training for High Fidelity Natural Image Synthesis

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T18:36:29.244231Z

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Observation 56047254-94f8-430c-9436-3d9cd432b85a · outbound

This paper cites In: IEEE/CVF Computer Vision and Pattern Recognition Conference, pp.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs In: IEEE/CVF Computer Vision and Pattern Recognition Conference, pp

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:36:29.944223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c15fb994-3605-4a02-aa76-93b79c08a931 · outbound

This paper cites Mode Regularized Generative Adversarial Networks.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs Mode Regularized Generative Adversarial Networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T18:36:29.253297Z

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Observation 4cf5310e-d55e-4c4d-aecd-5f2d115d4cd6 · outbound

This paper cites In: Conference on Neural Information Processing Systems, pp.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs In: Conference on Neural Information Processing Systems, pp

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:36:29.930156Z

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Observation 0a7939cc-99a0-462c-a29d-cd0136c478a6 · outbound

This paper cites Gradient descent GAN optimization is locally stable.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs Gradient descent GAN optimization is locally stable

Reference 16

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Observation 46abc7e7-902e-4b15-88af-24351845540a · outbound

This paper cites The Numerics of GANs.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs The Numerics of GANs

Reference 17

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Source-reported events for the cited work

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Observation 6841dcb3-1836-448b-91b7-71eb32dea3a0 · outbound

This paper cites Lipschitz regularized Deep Neural Networks generalize and are adversarially robust.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs Lipschitz regularized Deep Neural Networks generalize and are adversarially robust

Reference 18

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Observation 1f622327-475d-4812-932f-b466101c9e36 · outbound

This paper cites Lipschitz regularity of deep neural networks: analysis and efficient estimation.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs Lipschitz regularity of deep neural networks: analysis and efficient estimation

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 645be857-5b66-471b-a023-01393d525a3b · outbound

This paper cites Understanding the Effectiveness of Lipschitz-Continuity in Generative Adversarial Nets.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs Understanding the Effectiveness of Lipschitz-Continuity in Generative Adversarial Nets

Reference 20

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Observation 7cd42e4d-c113-43d6-90f2-2a79db73ed47 · outbound

This paper cites In: International Conference on Machine Learning, pp.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs In: International Conference on Machine Learning, pp

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:36:29.916664Z

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Observation d2f0f9fc-82a7-491f-9d6a-e4ecbc3a6902 · outbound

This paper cites Local Lipschitz Bounds of Deep Neural Networks.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs Local Lipschitz Bounds of Deep Neural Networks

Reference 22

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Observation 964cf1bb-ba8f-465d-9666-dfe49496378f · outbound

This paper cites In: International Conference on Machine Learning, pp.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs In: International Conference on Machine Learning, pp

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:36:29.901762Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6c434cfa-4332-4c86-a786-05de08dd65f2 · outbound

This paper cites Spectral Normalization for Generative Adversarial Networks.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs Spectral Normalization for Generative Adversarial Networks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T18:36:29.298422Z

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Observation 6ef2db4c-29a9-44f2-bc9d-b238d7dc57b1 · outbound

This paper cites In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:36:29.885971Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f0a60257-196a-4f32-b204-4e1098a942e4 · outbound

This paper cites In: InternationalConference on Computer Vision, pp.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs In: InternationalConference on Computer Vision, pp

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:36:29.871608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a3ba27c9-45d0-4444-9c0a-a73c90d55968 · outbound

This paper cites Conference on Neural Information Processing Systems 35, 8868–8881 (2022).

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs Conference on Neural Information Processing Systems 35, 8868–8881 (2022)

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:36:29.857746Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 57b64fb0-b1e7-4e46-9020-9499bae450f8 · outbound

This paper cites In: IEEE/CVF Computer Vision and Pattern Recognition Conference, pp.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs In: IEEE/CVF Computer Vision and Pattern Recognition Conference, pp

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:36:29.843815Z

Source-reported events for the cited work

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Observation c3a5f767-e4df-4348-a05a-479a4d649c80 · outbound

This paper cites Unrolled Generative Adversarial Networks.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs Unrolled Generative Adversarial Networks

Reference 29

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Observation c7318351-7bf1-402c-b9fa-6f7578897557 · outbound

This paper cites IID-GAN: an IID Sampling Perspective for Regularizing Mode Collapse.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs IID-GAN: an IID Sampling Perspective for Regularizing Mode Collapse

Reference 30

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T18:36:29.470841Z

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Observation 266c69c9-e14d-449c-91d2-35b1b3d41a4d · outbound

This paper cites Proceedings of the IEEE 86(11), 2278–2324 (1998) 24.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs Proceedings of the IEEE 86(11), 2278–2324 (1998) 24

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:36:29.829260Z

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Observation 73fb6594-1064-4fea-874b-1a3812c3462a · outbound

This paper cites Master’s thesis, Department of Computer Science, University of Toronto (2009).

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs Master’s thesis, Department of Computer Science, University of Toronto (2009)

Reference 32

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Observation 9dc4abaf-e790-498a-8696-6f2623340e74 · outbound

This paper cites LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T18:36:29.340251Z

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Unavailable: canonical work link unavailable.

Observation 73a5b114-d58c-4267-b02d-4d039a5301fe · outbound

This paper cites In: IEEE/CVF Computer Vision and Pattern Recognition Conference, pp.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs In: IEEE/CVF Computer Vision and Pattern Recognition Conference, pp

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:36:29.806423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ef14e039-85e8-4e20-9bdf-2094076ead9e · outbound

This paper cites In: InternationalConference on Computer Vision, pp.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs In: InternationalConference on Computer Vision, pp

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:36:29.791043Z

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Observation 240fcf3d-3e79-404a-938c-a464dfaeb17f · outbound

This paper cites Geometric GAN.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs Geometric GAN

Reference 36

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Observation eaffb130-7f86-4e20-ab84-431125b9cb81 · outbound

This paper cites In: IEEE/CVF Computer Vision and Pattern Recognition Conference, pp.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs In: IEEE/CVF Computer Vision and Pattern Recognition Conference, pp

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:36:29.775248Z

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Observation 35ef221e-689d-41cf-8891-bcb68a0715a8 · outbound

This paper cites Tackling the Generative Learning Trilemma with Denoising Diffusion GANs.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-10T18:36:29.362552Z

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Observation 22d17997-3004-4e8f-b997-a21e284981e8 · outbound

This paper cites Conference on Neural Information Processing Systems 29, 2234–2242 (2016).

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs Conference on Neural Information Processing Systems 29, 2234–2242 (2016)

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:36:29.760998Z

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Observation e890c2c5-c48c-4173-8bb3-bdb085496012 · outbound

This paper cites Conference on Neural Information Processing Systems 30 (2017).

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs Conference on Neural Information Processing Systems 30 (2017)

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:36:29.745561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1b2a7b37-5c06-4016-a75f-35c8f2bf89ef · outbound

This paper cites Conference on Neural Information Processing Systems 32 (2019).

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs Conference on Neural Information Processing Systems 32 (2019)

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:36:29.731056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation adf954ee-00ea-4609-a9ed-483885456e7e · outbound

This paper cites ∇θ 1 2 MP m=1 ηmgm (Gm(z)) 2# 0   =   MP m=1 ηmgm (Gm(z)) ∇θ.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs ∇θ 1 2 MP m=1 ηmgm (Gm(z)) 2# 0   =   MP m=1 ηmgm (Gm(z)) ∇θ

Reference 42

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

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