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Paper Citation Record · LEDGER

Nested Annealed Training Scheme for Generative Adversarial Networks

As of 12 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 0 inbound Pith citation observations for arXiv:2501.11318.

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

pith.paper-citation-record.v1
2501.11318 v1

Coverage vector

measured 67 of 67 reference resolution

Typed states for the displayed outbound observations.

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measured 67 of 67 standing notices

One-hop event checks from named stored sources.

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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

67 of 67 outbound references displayed

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External citation measurements

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Outbound references

Observation ad09071a-2860-440d-9160-9e4b87bfc061 · outbound

This paper cites Composite functional gradient learning of generative adversarial models,.

Nested Annealed Training Scheme for Generative Adversarial Networks Composite functional gradient learning of generative adversarial models,

Reference 1

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Observation 2c69f398-ca49-40e2-9393-7875169fc618 · outbound

This paper cites Imagen Video: High Definition Video Generation with Diffusion Models.

Nested Annealed Training Scheme for Generative Adversarial Networks Imagen Video: High Definition Video Generation with Diffusion Models

Reference 2

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Observation 105e413c-8c26-4557-b719-42ada1790bc0 · outbound

This paper cites Zero-shot text-to-image generation,.

Nested Annealed Training Scheme for Generative Adversarial Networks Zero-shot text-to-image generation,

Reference 3

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Observation 6db7c870-625a-4e02-ba4a-bf4d60b8b352 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Nested Annealed Training Scheme for Generative Adversarial Networks Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 4

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Observation b89a0cf5-3ad8-4f75-ac93-def622830ec8 · outbound

This paper cites Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold.

Nested Annealed Training Scheme for Generative Adversarial Networks Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold

Reference 5

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Observation 43f742f8-b63f-41b5-a747-fe7d79a6f800 · outbound

This paper cites LDM3D: Latent Diffusion Model for 3D.

Nested Annealed Training Scheme for Generative Adversarial Networks LDM3D: Latent Diffusion Model for 3D

Reference 6

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Observation aa4acfdf-87be-4dba-a25b-9a89f32065e4 · outbound

This paper cites Locally Attentional SDF Diffusion for Controllable 3D Shape Generation.

Nested Annealed Training Scheme for Generative Adversarial Networks Locally Attentional SDF Diffusion for Controllable 3D Shape Generation

Reference 7

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Observation 3f2b381d-1f3e-4c7a-bd40-67b278b486d3 · outbound

This paper cites Mm-diffusion: Learning multi-modal diffusion models for joint audio and video generation,.

Nested Annealed Training Scheme for Generative Adversarial Networks Mm-diffusion: Learning multi-modal diffusion models for joint audio and video generation,

Reference 8

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Observation 181c22d1-549b-4841-8958-1ace885034aa · outbound

This paper cites Generative adversarial nets,.

Nested Annealed Training Scheme for Generative Adversarial Networks Generative adversarial nets,

Reference 9

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Observation ddda6080-3e07-4e28-8298-9f244146d739 · outbound

This paper cites Statistics enhancement generative adversarial networks for diverse con- ditional image synthesis,.

Nested Annealed Training Scheme for Generative Adversarial Networks Statistics enhancement generative adversarial networks for diverse con- ditional image synthesis,

Reference 10

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Observation be10f078-84ca-45ce-bc03-c0c4e24ca08a · outbound

This paper cites Mitigating label noise in gans via enhanced spectral normalization,.

Nested Annealed Training Scheme for Generative Adversarial Networks Mitigating label noise in gans via enhanced spectral normalization,

Reference 11

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Observation 8b97a865-fdd6-4719-b9d9-932a451def2b · outbound

This paper cites Hrinversion: High- resolution gan inversion for cross-domain image synthesis,.

Nested Annealed Training Scheme for Generative Adversarial Networks Hrinversion: High- resolution gan inversion for cross-domain image synthesis,

Reference 12

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Observation 8338877f-ad24-4154-ac4a-70224d362a36 · outbound

This paper cites A framework of composite functional gradient methods for generative adversarial models,.

Nested Annealed Training Scheme for Generative Adversarial Networks A framework of composite functional gradient methods for generative adversarial models,

Reference 13

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Observation 2fe09783-9d44-44c8-9a11-e4f2aba9c241 · outbound

This paper cites Generative modeling by estimating gradients of the data distribution,.

Nested Annealed Training Scheme for Generative Adversarial Networks Generative modeling by estimating gradients of the data distribution,

Reference 14

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Observation d349bcdd-9726-46a6-b5d1-a1f9cb8fc8f7 · outbound

This paper cites Annealed importance sampling,.

Nested Annealed Training Scheme for Generative Adversarial Networks Annealed importance sampling,

Reference 15

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Source-reported events for the cited work

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Observation 96e16427-c28d-4db5-b8f1-22837f320e7f · outbound

This paper cites Optimization by simulated annealing,.

Nested Annealed Training Scheme for Generative Adversarial Networks Optimization by simulated annealing,

Reference 16

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Observation c93e4cfb-2e20-4ae7-91c8-e4dd013fc175 · outbound

This paper cites Learning multiple layers of features from tiny images,.

Nested Annealed Training Scheme for Generative Adversarial Networks Learning multiple layers of features from tiny images,

Reference 17

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Observation 336f2c50-231b-4b87-849f-32b1f1185649 · outbound

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

Nested Annealed Training Scheme for Generative Adversarial Networks LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

Reference 18

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Observation 2cebe1c6-727e-46e9-97ab-53acaec41c49 · outbound

This paper cites Deep learning face attributes in the wild,.

Nested Annealed Training Scheme for Generative Adversarial Networks Deep learning face attributes in the wild,

Reference 19

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Observation d97be35c-65b7-4169-8312-74b021675edf · outbound

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Nested Annealed Training Scheme for Generative Adversarial Networks Imagenet: A large-scale hierarchical image database,

Reference 20

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Observation b892d484-4405-47d5-8ab6-c3b9ee6eae65 · outbound

This paper cites Improved Training of Wasserstein GANs.

Nested Annealed Training Scheme for Generative Adversarial Networks Improved Training of Wasserstein GANs

Reference 21

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Observation 5db33a39-6662-4544-a088-435c09024c1b · outbound

This paper cites Least squares generative adversarial networks,.

Nested Annealed Training Scheme for Generative Adversarial Networks Least squares generative adversarial networks,

Reference 22

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Observation 27003d2c-d181-4f63-9685-ab928e2a8384 · outbound

This paper cites Geometric GAN.

Nested Annealed Training Scheme for Generative Adversarial Networks Geometric GAN

Reference 23

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Observation 47b64600-10e8-42ac-a83d-9d5ad2a05c8b · outbound

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

Nested Annealed Training Scheme for Generative Adversarial Networks Large Scale GAN Training for High Fidelity Natural Image Synthesis

Reference 24

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Observation 3c3e6d3e-f062-405a-87ac-4abf2f20d96e · outbound

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

Nested Annealed Training Scheme for Generative Adversarial Networks Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 25

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Observation c3d87129-3327-44b3-9c86-2b28a33af345 · outbound

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Nested Annealed Training Scheme for Generative Adversarial Networks Improved techniques for training gans,

Reference 26

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Observation 69245330-33c1-441a-9fb4-de974ff43a54 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium,.

Nested Annealed Training Scheme for Generative Adversarial Networks Gans trained by a two time-scale update rule converge to a local nash equilibrium,

Reference 27

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Observation a285c952-efab-4cf5-95ce-235ed23f3b7c · outbound

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Nested Annealed Training Scheme for Generative Adversarial Networks Improved techniques for training score-based generative models,

Reference 28

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Observation 00625eaa-3c0d-4503-9486-861ccebc5627 · outbound

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Nested Annealed Training Scheme for Generative Adversarial Networks Deep unsupervised learning using nonequilibrium thermodynamics,

Reference 29

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Observation b1483c6f-5529-48af-abb3-774195c3b3b9 · outbound

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Nested Annealed Training Scheme for Generative Adversarial Networks Denoising diffusion probabilistic models,

Reference 30

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Observation 265638bf-1d0b-4b2b-ab88-30ff8b2ad261 · outbound

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Nested Annealed Training Scheme for Generative Adversarial Networks Score-Based Generative Modeling through Stochastic Differential Equations

Reference 31

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Observation 4414cea8-adfb-4285-ac3b-9dc6397db923 · outbound

This paper cites Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models.

Nested Annealed Training Scheme for Generative Adversarial Networks Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models

Reference 32

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Nested Annealed Training Scheme for Generative Adversarial Networks High- resolution image synthesis with latent diffusion models,

Reference 33

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Observation fabadd1a-e612-41a5-87d3-4906b593acd3 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding,.

Nested Annealed Training Scheme for Generative Adversarial Networks Photorealistic text-to-image diffusion models with deep language understanding,

Reference 34

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Observation afa27f4e-779d-43f8-9337-cacbc1303b81 · outbound

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Nested Annealed Training Scheme for Generative Adversarial Networks Towards high-quality hdr deghosting with conditional diffusion models,

Reference 35

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Observation bfea9f83-25db-40b9-b4ad-0e98b041c311 · outbound

This paper cites Adaptive conditional denoising diffusion model with hybrid affinity regularizer for generalized zero-shot learning,.

Nested Annealed Training Scheme for Generative Adversarial Networks Adaptive conditional denoising diffusion model with hybrid affinity regularizer for generalized zero-shot learning,

Reference 36

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

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Observation 2be92b79-4441-4db5-b60b-039b3a1efd57 · outbound

This paper cites Games of gans: Game-theoretical models for generative adversarial networks,.

Nested Annealed Training Scheme for Generative Adversarial Networks Games of gans: Game-theoretical models for generative adversarial networks,

Reference 37

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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.

source=pdf_text observed=2026-08-10T18:30:58.476211Z digest=sha256:3230c1b27a6becbc879eda48481c7c6cd647e4243c9ce11e4b53c87a1b6f1e6c

Observation e8a4029d-105c-4b06-a122-b04d7f3caeeb · outbound

This paper cites Training Generative Adversarial Networks via stochastic Nash games.

Nested Annealed Training Scheme for Generative Adversarial Networks Training Generative Adversarial Networks via stochastic Nash games

Reference 38

Resolution
verified exact
local_arxiv, observed 2026-08-10T18:30:58.804063Z

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.

source=pdf_text observed=2026-08-10T18:30:58.479886Z digest=sha256:8e73d69adff3a32d381eea603dbfa5b486c638a97e89f1f6993e7de4a8791348

Observation 7efcf0a3-9afc-438b-9110-24f2b642a910 · outbound

This paper cites Stackelberg GAN: Towards Provable Minimax Equilibrium via Multi-Generator Architectures.

Nested Annealed Training Scheme for Generative Adversarial Networks Stackelberg GAN: Towards Provable Minimax Equilibrium via Multi-Generator Architectures

Reference 39

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verified exact
local_arxiv, observed 2026-08-10T18:30:58.787006Z

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.

source=pdf_text observed=2026-08-10T18:30:58.483695Z digest=sha256:f06ad30c5a8523a344bc4d28ca7faa5c296d326cbef74eb566147cfa33926da3

Observation e35786b4-0cc6-4fdd-84dd-56146cb169d3 · outbound

This paper cites Finding mixed nash equilibria of generative adversarial networks,.

Nested Annealed Training Scheme for Generative Adversarial Networks Finding mixed nash equilibria of generative adversarial networks,

Reference 40

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raw_fallback, observed 2026-08-10T18:30:59.155568Z

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.

source=pdf_text observed=2026-08-10T18:30:58.487932Z digest=sha256:1107dee4dd3c385febbd7fc1e4e8369857acc4ca68e75efe8006cd159901a84c

Observation 7ae1dac9-baa4-4033-bd1c-924a660e21e5 · outbound

This paper cites FedGAN: Federated Generative Adversarial Networks for Distributed Data.

Nested Annealed Training Scheme for Generative Adversarial Networks FedGAN: Federated Generative Adversarial Networks for Distributed Data

Reference 41

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no resolver link, observed 2026-08-10T18:30:58.492134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T18:30:58.492134Z digest=sha256:10ed700b6a5783a1b7b293814d74d4a50395d6644a304ed39becadccb2ec28e6

Observation 8fa9095c-ca06-40cc-9de7-267014027055 · outbound

This paper cites Dual discriminator generative adversarial nets,.

Nested Annealed Training Scheme for Generative Adversarial Networks Dual discriminator generative adversarial nets,

Reference 42

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verified fuzzy
raw_fallback, observed 2026-08-10T18:30:59.144266Z

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.

source=pdf_text observed=2026-08-10T18:30:58.496396Z digest=sha256:29d9664182aa40486403a57b90f0ee1505bab4810201cb6b816fa308e306b9a9

Observation 12e5ecff-ab12-44a0-9566-a4826ad4f89d · outbound

This paper cites Consistency of multiagent distributed generative adversarial networks,.

Nested Annealed Training Scheme for Generative Adversarial Networks Consistency of multiagent distributed generative adversarial networks,

Reference 43

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verified fuzzy
raw_fallback, observed 2026-08-10T18:30:59.133193Z

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.

source=pdf_text observed=2026-08-10T18:30:58.500682Z digest=sha256:d8c5b5b36fcf086e84730ae58a70fc2e6878bae86ec6941f3187696cb5ff2981

Observation 824dc8ca-e5c5-4e2d-a032-d4ba7088592b · outbound

This paper cites Triple generative adversarial nets,.

Nested Annealed Training Scheme for Generative Adversarial Networks Triple generative adversarial nets,

Reference 44

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verified fuzzy
raw_fallback, observed 2026-08-10T18:30:59.121745Z

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.

source=pdf_text observed=2026-08-10T18:30:58.504956Z digest=sha256:5f531d5948c3829491d62e3a972cf51eab79874fc619f88bfe642f6f686bfcd6

Observation 30452ff1-8570-4682-ad94-e5a76c3b3e7c · outbound

This paper cites Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step.

Nested Annealed Training Scheme for Generative Adversarial Networks Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step

Reference 45

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no resolver link, observed 2026-08-10T18:30:58.509150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T18:30:58.509150Z digest=sha256:7f278f6c59c8041353318ec0eeeff7c72d94ac17617c667188b182ead8ab1538

Observation b048f87b-3cbe-486b-9295-90c512c8b3b6 · outbound

This paper cites BEGAN: Boundary Equilibrium Generative Adversarial Networks.

Nested Annealed Training Scheme for Generative Adversarial Networks BEGAN: Boundary Equilibrium Generative Adversarial Networks

Reference 46

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unresolved
no resolver link, observed 2026-08-10T18:30:58.513551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T18:30:58.513551Z digest=sha256:69d94ac5fa3f86b7c1b815f294fca2d5963099957f5b2a709055de7e93d6ab8e

Observation d78b7010-2d0f-4273-af59-833c493cf3b5 · outbound

This paper cites An Online Learning Approach to Generative Adversarial Networks.

Nested Annealed Training Scheme for Generative Adversarial Networks An Online Learning Approach to Generative Adversarial Networks

Reference 47

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no resolver link, observed 2026-08-10T18:30:58.518025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T18:30:58.518025Z digest=sha256:66baf56ab95609e2f944ae37804d31480b6048e8620890661c61ef20e7421f18

Observation f0d60e53-39ac-4624-ac0a-725973824879 · outbound

This paper cites Fictitious gan: Training gans with historical models,.

Nested Annealed Training Scheme for Generative Adversarial Networks Fictitious gan: Training gans with historical models,

Reference 48

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verified fuzzy
raw_fallback, observed 2026-08-10T18:30:59.110281Z

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.

source=pdf_text observed=2026-08-10T18:30:58.522311Z digest=sha256:9f868c61cd15d38f73da7d40279f5cd1868ec00f3635e5125b58852da8c5375b

Observation 9478d9cc-03d6-4a12-8494-550748a6f5b2 · outbound

This paper cites Image captioning using adversarial networks and reinforcement learning,.

Nested Annealed Training Scheme for Generative Adversarial Networks Image captioning using adversarial networks and reinforcement learning,

Reference 49

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raw_fallback, observed 2026-08-10T18:30:59.098244Z

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.

source=pdf_text observed=2026-08-10T18:30:58.527650Z digest=sha256:21003430104dd549c218f76d34febc9e65232fe67096959ad7d44cc841c24fa3

Observation d3f08df0-ed6e-4ecd-8c7c-5ad2e88980d2 · outbound

This paper cites Mode Regularized Generative Adversarial Networks.

Nested Annealed Training Scheme for Generative Adversarial Networks Mode Regularized Generative Adversarial Networks

Reference 50

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no resolver link, observed 2026-08-10T18:30:58.532145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T18:30:58.532145Z digest=sha256:714f2374d6316861406cd9b7060edd74f1101a0a71f46137df20cfc4b96dcc86

Observation f4211818-d34e-427e-8eb3-d67d885d26ec · outbound

This paper cites Smoothness and Stability in GANs.

Nested Annealed Training Scheme for Generative Adversarial Networks Smoothness and Stability in GANs

Reference 51

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no resolver link, observed 2026-08-10T18:30:58.536947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T18:30:58.536947Z digest=sha256:74841403d1197014785085955f0243313e86c47f632330b2d782c8be91f84980

Observation 96653c31-3b57-4bd6-b95c-e9af46190b4d · outbound

This paper cites Gradient descent GAN optimization is locally stable.

Nested Annealed Training Scheme for Generative Adversarial Networks Gradient descent GAN optimization is locally stable

Reference 52

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local_arxiv, observed 2026-08-10T18:30:58.698286Z

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.

source=pdf_text observed=2026-08-10T18:30:58.541245Z digest=sha256:cb04bd34ccc643177c2796de2c7bb00e635ec7ea2699c62234d6ee34cd258095

Observation 692e8164-c2c4-4b1f-a3ac-c7d248aec02a · outbound

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

Nested Annealed Training Scheme for Generative Adversarial Networks Stabilizing Training of Generative Adversarial Networks through Regularization

Reference 53

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no resolver link, observed 2026-08-10T18:30:58.545367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T18:30:58.545367Z digest=sha256:068d8379e61cbc4e4ce118a7f4811efc2171e7265a39ff89dffe3dd9873d7b6e

Observation d1b72005-0f7b-46c0-b3ba-3bf502195fae · outbound

This paper cites Which training methods for gans do actually converge?.

Nested Annealed Training Scheme for Generative Adversarial Networks Which training methods for gans do actually converge?

Reference 54

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verified fuzzy
raw_fallback, observed 2026-08-10T18:30:59.086110Z

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.

source=pdf_text observed=2026-08-10T18:30:58.549441Z digest=sha256:c258f3ccfd1c36438e0c2bdb9fe84745d3ff0eaae31cebc8e20d8001cc41821e

Observation 89931c6a-e0a5-44ec-b1cc-350469f4c77b · outbound

This paper cites Wasserstein generative ad- versarial networks,.

Nested Annealed Training Scheme for Generative Adversarial Networks Wasserstein generative ad- versarial networks,

Reference 55

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raw_fallback, observed 2026-08-10T18:30:59.073607Z

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.

source=pdf_text observed=2026-08-10T18:30:58.553781Z digest=sha256:5a2d0db28601b0b82ce006177fe9981dcdb217e4843c92f891599d807b603cbe

Observation 1c103dd8-e97a-46b4-8502-8cd0e1a7e7c3 · outbound

This paper cites Hierarchical implicit models and likelihood-free variational inference,.

Nested Annealed Training Scheme for Generative Adversarial Networks Hierarchical implicit models and likelihood-free variational inference,

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T18:30:59.059233Z

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.

source=pdf_text observed=2026-08-10T18:30:58.557674Z digest=sha256:94ef4df583bb63a3eaa227bda6d4e1121aa9311bc2e4d5b3b155cc15f70d65fc

Observation 01eb4d06-bac6-4c39-b00a-2847b0d8dd9c · outbound

This paper cites f-gan: Training generative neu- ral samplers using variational divergence minimization,.

Nested Annealed Training Scheme for Generative Adversarial Networks f-gan: Training generative neu- ral samplers using variational divergence minimization,

Reference 57

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verified fuzzy
raw_fallback, observed 2026-08-10T18:30:59.047128Z

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.

source=pdf_text observed=2026-08-10T18:30:58.561338Z digest=sha256:07947561406f518d0be06bfc8487d0d0a7c0eaf9670a0f48f3f3746770c50dfb

Observation de9581ff-f4b7-41bb-8de1-5874a492d680 · outbound

This paper cites Varia- tional inference via wasserstein gradient flows,.

Nested Annealed Training Scheme for Generative Adversarial Networks Varia- tional inference via wasserstein gradient flows,

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T18:30:59.026900Z

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.

source=pdf_text observed=2026-08-10T18:30:58.564910Z digest=sha256:bf120f9c06585207a93593f43bfcc53b3add26c67a6a2ce568c800c080ad4e8a

Observation 6d01bca7-e550-4459-b7ea-7a7e243447c9 · outbound

This paper cites Variational Wasserstein gradient flow.

Nested Annealed Training Scheme for Generative Adversarial Networks Variational Wasserstein gradient flow

Reference 59

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation b4398022-c70a-48ab-9677-8785ce4ca0c8 · outbound

This paper cites Deep gener- ative learning via variational gradient flow,.

Nested Annealed Training Scheme for Generative Adversarial Networks Deep gener- ative learning via variational gradient flow,

Reference 60

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raw_fallback, observed 2026-08-10T18:30:59.014918Z

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.

source=pdf_text observed=2026-08-10T18:30:58.573460Z digest=sha256:488fe828a9433ee2b7348b6d794049f785cf192638cd85907704ba486f7c13ae

Observation 867534c0-d676-45ea-a44d-28751d214d97 · outbound

This paper cites Gradient layer: Enhancing the convergence of adversarial training for generative models,.

Nested Annealed Training Scheme for Generative Adversarial Networks Gradient layer: Enhancing the convergence of adversarial training for generative models,

Reference 61

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verified fuzzy
raw_fallback, observed 2026-08-10T18:30:59.002394Z

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.

source=pdf_text observed=2026-08-10T18:30:58.577099Z digest=sha256:6a9138332f5dca44b8f4fb8a12c932f8e401761c5d4aef0bd0cc26935cfec288

Observation bda09316-348e-42db-a7c8-90e9cb9f5da4 · outbound

This paper cites How well generative adversarial networks learn distributions,.

Nested Annealed Training Scheme for Generative Adversarial Networks How well generative adversarial networks learn distributions,

Reference 62

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verified fuzzy
raw_fallback, observed 2026-08-10T18:30:58.989077Z

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.

source=pdf_text observed=2026-08-10T18:30:58.581075Z digest=sha256:f252714f2dc5134e3bd1995b55eef89b8bc5f501376283e1adbd11a89db15221

Observation 025fabe2-811c-46fb-84a5-348601907430 · outbound

This paper cites An error analysis of generative adversarial networks for learning distributions,.

Nested Annealed Training Scheme for Generative Adversarial Networks An error analysis of generative adversarial networks for learning distributions,

Reference 63

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verified fuzzy
raw_fallback, observed 2026-08-10T18:30:58.976571Z

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.

source=pdf_text observed=2026-08-10T18:30:58.585149Z digest=sha256:2df5c74daabfa609414a15d7b9f8db778749e0d2b0aab8429c3533205f855dd0

Observation dccf3e58-75d5-4cde-b2ee-734bd198d19c · outbound

This paper cites Error analysis of generative adversarial network.

Nested Annealed Training Scheme for Generative Adversarial Networks Error analysis of generative adversarial network

Reference 64

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verified exact
local_arxiv, observed 2026-08-10T18:30:58.659338Z

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.

source=pdf_text observed=2026-08-10T18:30:58.589504Z digest=sha256:6cff31976f739b8216f502741c44b9f0655d16d50d7a7c3a4d29c0a11751d03a

Observation f6486687-8ee9-431f-87cd-2a69bdd0f3ff · outbound

This paper cites Training generative adversarial networks in one stage,.

Nested Annealed Training Scheme for Generative Adversarial Networks Training generative adversarial networks in one stage,

Reference 65

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verified fuzzy
raw_fallback, observed 2026-08-10T18:30:58.963576Z

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.

source=pdf_text observed=2026-08-10T18:30:58.593870Z digest=sha256:984dbd83d0c49041adc08379c6648e30e7c34c4c8c79ed1adb15850e9973faed

Observation cd874062-d76e-451a-acee-e225d4d85bc4 · outbound

This paper cites The Numerics of GANs.

Nested Annealed Training Scheme for Generative Adversarial Networks The Numerics of GANs

Reference 66

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verified exact
local_arxiv, observed 2026-08-10T18:30:58.641141Z

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.

source=pdf_text observed=2026-08-10T18:30:58.597729Z digest=sha256:cd8e36937084cc245a3c0c04691536ee813f8cddbf528383fbf471ed2c217207

Observation 3f5fc6da-6cf9-4219-b33f-c50045f502de · outbound

This paper cites His research interests include intelligent information processing and geographic information systems.

Nested Annealed Training Scheme for Generative Adversarial Networks His research interests include intelligent information processing and geographic information systems

Reference 2006

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verified fuzzy
raw_fallback, observed 2026-08-10T18:30:58.950183Z

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.

source=pdf_text observed=2026-08-10T18:30:58.602071Z digest=sha256:3e66479c46e5ff3462b2310d2a2b0bf3a0823ed05530b7ac72d27cd65d6e8ce1

Pith citing papers

No inbound Pith citation observations are available.