Paper Citation Record · LEDGER

Augmented Conditioning Is Enough For Effective Training Image Generation

As of 9 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2502.04475.

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

pith.paper-citation-record.v1
2502.04475 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T22:39:57.904631Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

17 of 17 outbound references displayed

verified exact0
verified fuzzy2
unresolved14
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b9bcc7b5-2c49-4cdb-b53c-c5b9af3756fe · outbound

This paper cites RandAugment: Practical automated data augmentation with a reduced search space.

Augmented Conditioning Is Enough For Effective Training Image Generation RandAugment: Practical automated data augmentation with a reduced search space

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-08T22:39:57.804339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc36b55e-fd09-4c99-9ede-2c9871b7f06f · outbound

This paper cites an unresolved cited work.

Augmented Conditioning Is Enough For Effective Training Image Generation Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-08T22:39:58.255312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 40274e11-b9f0-4219-995a-c6daa63e6b6b · outbound

This paper cites Fergus, and P.

Augmented Conditioning Is Enough For Effective Training Image Generation Fergus, and P

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:39:58.288302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ed3f4820-a3fb-4b24-88f6-b18dfc72d9f7 · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

Augmented Conditioning Is Enough For Effective Training Image Generation SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-08T22:39:57.875388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 905f2f7d-2510-4f85-ba8a-9a3a6233207c · outbound

This paper cites Dropout: A simple way to prevent neural networks from overfitting.

Augmented Conditioning Is Enough For Effective Training Image Generation Dropout: A simple way to prevent neural networks from overfitting

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:39:58.272862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2075470d-8b61-4f1f-9fef-f135d871548d · outbound

This paper cites Effective Data Augmentation With Diffusion Models.

Augmented Conditioning Is Enough For Effective Training Image Generation Effective Data Augmentation With Diffusion Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-08T22:39:57.890489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b380230a-a95e-41a9-926d-84e503a1da1c · outbound

This paper cites Aggregated Residual Transformations for Deep Neural Networks.

Augmented Conditioning Is Enough For Effective Training Image Generation Aggregated Residual Transformations for Deep Neural Networks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-08T22:39:57.900181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd45e4a0-5340-4984-a7c2-cf6dd16a4924 · outbound

This paper cites 2004.383.

Augmented Conditioning Is Enough For Effective Training Image Generation 2004.383

Reference 2004

Resolution: malformed identifier; no resolver link, observed 2026-08-08T22:39:57.825480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ef6cec5-5363-439d-94f0-fe59256737d4 · outbound

This paper cites Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach.

Augmented Conditioning Is Enough For Effective Training Image Generation Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach

Reference 2008

Resolution: unresolved; no resolver link, observed 2026-08-08T22:39:57.869850Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e25e9d8-55a8-4327-8920-336dde278794 · outbound

This paper cites Fei Du, Peng Yang, Qi Jia, Fengtao Nan, Xiaoting Chen, and Yun Yang.

Augmented Conditioning Is Enough For Effective Training Image Generation Fei Du, Peng Yang, Qi Jia, Fengtao Nan, Xiaoting Chen, and Yun Yang

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-08T22:39:57.809966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20fa9d6a-acb8-439c-a437-04ff02ba1d5f · outbound

This paper cites Lijie Fan, Kaifeng Chen, Dilip Krishnan, Dina Katabi, Phillip Isola, and Yonglong Tian.

Augmented Conditioning Is Enough For Effective Training Image Generation Lijie Fan, Kaifeng Chen, Dilip Krishnan, Dina Katabi, Phillip Isola, and Yonglong Tian

Reference 2010

Resolution: unresolved; no resolver link, observed 2026-08-08T22:39:57.815045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1631dc15-8f67-4a2f-ae29-f009326af876 · outbound

This paper cites Microsoft COCO: Common Objects in Context.

Augmented Conditioning Is Enough For Effective Training Image Generation Microsoft COCO: Common Objects in Context

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-08T22:39:57.845536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d484ecfa-c032-4922-9309-c0ffb6a926f6 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Augmented Conditioning Is Enough For Effective Training Image Generation Deep Residual Learning for Image Recognition

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-08T22:39:57.836139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97f71c3e-7a44-4e25-a25f-cea44feb9452 · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

Augmented Conditioning Is Enough For Effective Training Image Generation SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-08T22:39:57.856083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef8f9aaa-c8b0-40f2-9a22-b603020bc36f · outbound

This paper cites Effectively Unbiased FID and Inception Score and where to find them.

Augmented Conditioning Is Enough For Effective Training Image Generation Effectively Unbiased FID and Inception Score and where to find them

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-08T22:39:57.799991Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 468ac561-84aa-4874-9326-18f51a5e062b · outbound

This paper cites SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations.

Augmented Conditioning Is Enough For Effective Training Image Generation SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-08T22:39:57.865375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a27a5b46-8859-413c-86fe-07557dea0ff1 · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

Augmented Conditioning Is Enough For Effective Training Image Generation U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-08T22:39:57.880635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.