Paper Citation Record · LEDGER

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data

As of 7 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2507.20782.

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

pith.paper-citation-record.v1
2507.20782 v1

Coverage vector

measured 41 of 41 reference resolution

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measured 41 of 41 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

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External citation measurements

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Outbound references

Observation 5a7d7afa-35c6-4f64-9eb7-3e0ba826c86c · outbound

This paper cites GANDiffFace: Control- lable generation of synthetic datasets for face recognition with gans and diffusion models,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data GANDiffFace: Control- lable generation of synthetic datasets for face recognition with gans and diffusion models,

Reference 1

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This paper cites WebFace260M: A benchmark unveiling the power of million-scale deep face recognition,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data WebFace260M: A benchmark unveiling the power of million-scale deep face recognition,

Reference 2

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Observation af48d7c2-58b4-45de-88e4-27cb6b0e6975 · outbound

This paper cites Digi2Real: Bridging the realism gap in synthetic-data face recognition via foundation models,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Digi2Real: Bridging the realism gap in synthetic-data face recognition via foundation models,

Reference 3

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This paper cites HyperFace: Generating synthetic face-recognition datasets by exploring the face-embedding hypersphere,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data HyperFace: Generating synthetic face-recognition datasets by exploring the face-embedding hypersphere,

Reference 4

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Observation 7a606d74-bc6b-4e05-919e-2a5ddda460dd · outbound

This paper cites DCFace: Synthetic face generation with dual condition diffusion model,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data DCFace: Synthetic face generation with dual condition diffusion model,

Reference 5

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Observation e834164a-ccf7-4c00-85bc-44089d157646 · outbound

This paper cites Idiff-face: Synthetic-based face recognition through fizzy identity- conditioned diffusion models,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Idiff-face: Synthetic-based face recognition through fizzy identity- conditioned diffusion models,

Reference 6

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Observation eaebb409-7c9f-49d0-9cbd-cd9f480cc7fd · outbound

This paper cites Synthetic face datasets generation via latent space exploration from brownian identity diffusion,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Synthetic face datasets generation via latent space exploration from brownian identity diffusion,

Reference 7

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This paper cites Variface: Fair and diverse synthetic dataset generation for face recognition,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Variface: Fair and diverse synthetic dataset generation for face recognition,

Reference 8

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Observation 66a6329d-0886-47e6-8999-f1f497d9b3b2 · outbound

This paper cites The Impact of Balancing Real and Synthetic Data on Accuracy and Fairness in Face Recognition.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data The Impact of Balancing Real and Synthetic Data on Accuracy and Fairness in Face Recognition

Reference 9

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Observation c3fdfb98-8e7a-4516-8d88-fe81666075bf · outbound

This paper cites Vulnerability of automatic identity recognition to audio-visual deep- fakes,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Vulnerability of automatic identity recognition to audio-visual deep- fakes,

Reference 10

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Observation 8731ee83-e11f-4324-b3ad-0355b712f62f · outbound

This paper cites Bias and diversity in synthetic-based face recognition,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Bias and diversity in synthetic-based face recognition,

Reference 11

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Observation 4872d053-befa-4c8b-a181-6850737a8c42 · outbound

This paper cites From Fake to Real: Pretraining on Balanced Synthetic Images to Prevent Spurious Correlations in Image Recognition.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data From Fake to Real: Pretraining on Balanced Synthetic Images to Prevent Spurious Correlations in Image Recognition

Reference 12

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Observation b16ac127-fe0b-4335-95e1-b1c2f8ab655f · outbound

This paper cites Review of Demographic Fairness in Face Recognition.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Review of Demographic Fairness in Face Recognition

Reference 13

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This paper cites 1.58-bit FLUX.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data 1.58-bit FLUX

Reference 14

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Observation 7d54ead8-427d-44ea-adaa-e51af9c477ec · outbound

This paper cites Scaling Rectified Flow Transformers for High-Resolution Image Synthesis.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Reference 15

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Observation 07dee50f-ae97-4bd5-bc18-a0850c05925f · outbound

This paper cites Arc2Face: A Foundation Model for ID-Consistent Human Faces.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Arc2Face: A Foundation Model for ID-Consistent Human Faces

Reference 16

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Observation feac9f06-e631-4ca5-b6fc-dd5d13c61bfa · outbound

This paper cites IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

Reference 17

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This paper cites Retinaface: Single-shot multi-level face localisation in the wild,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Retinaface: Single-shot multi-level face localisation in the wild,

Reference 18

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This paper cites Learning transferable visual models from natural language supervision,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Learning transferable visual models from natural language supervision,

Reference 19

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Observation 490a4e1c-4cff-4096-9645-0adfe15a97cb · outbound

This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Laion-5b: An open large-scale dataset for training next generation image-text models,

Reference 20

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Observation cc5ee4e9-b597-4973-85ed-a87b3d5777f3 · outbound

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Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Coyo-700m: Image-text pair dataset

Reference 21

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Observation 28da035b-5473-4ec1-93a2-dc55472ca994 · outbound

This paper cites Edgeface: Efficient face recognition model for edge devices,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Edgeface: Efficient face recognition model for edge devices,

Reference 22

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This paper cites Labeled faces in the wild: A database for studying face recognition in unconstrained environments,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Labeled faces in the wild: A database for studying face recognition in unconstrained environments,

Reference 23

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This paper cites Agedb: The first manually collected, in-the-wild age database,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Agedb: The first manually collected, in-the-wild age database,

Reference 24

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This paper cites IARPA Janus Benchmark — B (IJB-B): Face recognition benchmarking,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data IARPA Janus Benchmark — B (IJB-B): Face recognition benchmarking,

Reference 25

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Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data IARPA Janus Benchmark — C: Face recognition in video,

Reference 26

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This paper cites Racial faces in the wild: Reducing racial bias by information-maximization adaptation network,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Racial faces in the wild: Reducing racial bias by information-maximization adaptation network,

Reference 27

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Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Learning Face Representation from Scratch

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Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Analyzing and improving the image quality of styleGAN,

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This paper cites Demographic fairness transformer for bias mitigation in face recognition,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Demographic fairness transformer for bias mitigation in face recognition,

Reference 30

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This paper cites Synthetic data for the mitigation of demographic biases in face recognition,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Synthetic data for the mitigation of demographic biases in face recognition,

Reference 31

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This paper cites Frcsyn challenge at cvpr 2024: Face recognition challenge in the era of synthetic data,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Frcsyn challenge at cvpr 2024: Face recognition challenge in the era of synthetic data,

Reference 32

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Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data FRCSyn challenge at W ACV 2024: Face recognition challenge in the era of synthetic data,

Reference 33

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Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data FRCSyn-onGoing: Benchmarking and comprehensive evaluation of real and synthetic data to improve face recognition systems,

Reference 34

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Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data AdaFace: Quality adaptive margin for face recognition,

Reference 35

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Observation 2f9ad8af-9bb4-4bed-b73f-6c0f90937668 · outbound

This paper cites Arcface: Additive angular margin loss for deep face recognition,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Arcface: Additive angular margin loss for deep face recognition,

Reference 36

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This paper cites Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments

Reference 37

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This paper cites Cross-pose lfw: A database for studying cross-pose face recognition in unconstrained environments,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Cross-pose lfw: A database for studying cross-pose face recognition in unconstrained environments,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:19:36.117551Z

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Observation 9e216725-57fc-431c-b546-7d70fa1ddf68 · outbound

This paper cites Frontal to profile face verification in the wild,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Frontal to profile face verification in the wild,

Reference 39

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This paper cites Joint face detection and alignment using multitask cascaded convolutional networks,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Joint face detection and alignment using multitask cascaded convolutional networks,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:19:36.078838Z

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Observation 609a1bef-c3f9-4a36-8a17-b46f7d2119a5 · outbound

This paper cites Mitigating demographic bias in face recognition via regularized score calibration,.

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data Mitigating demographic bias in face recognition via regularized score calibration,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:19:36.056928Z

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Pith citing papers

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