Paper Citation Record · LEDGER

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives

As of 23 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2509.02029.

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

pith.paper-citation-record.v1
2509.02029 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T12:02:50.959883Z

measured 32 of 32 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

32 of 32 outbound references displayed

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External citation measurements

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Outbound references

Observation fdf3793c-2b47-4f62-a0f1-fe15224781e3 · outbound

This paper cites A theoretical analysis of contrastive unsupervised representation learning, 2019.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives A theoretical analysis of contrastive unsupervised representation learning, 2019

Reference 1

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Observation b605460c-4fb7-456a-8a94-8c0b3eafa626 · outbound

This paper cites This dataset does not exist: training models from generated images.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives This dataset does not exist: training models from generated images

Reference 2

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Observation bff3d71f-7849-419a-abcd-ea330d0940e6 · outbound

This paper cites Deep clustering for unsupervised learning of visual features, 2019.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives Deep clustering for unsupervised learning of visual features, 2019

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:02:52.850007Z

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Observation 7a6a780c-3e72-4d70-88fb-6adfc109a591 · outbound

This paper cites Unsupervised learn- ing of visual features by contrasting cluster assignments.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives Unsupervised learn- ing of visual features by contrasting cluster assignments

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:02:52.810016Z

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Observation d5774887-06a9-485a-9659-b7f945d3c9a7 · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers, 2021.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives Emerg- ing properties in self-supervised vision transformers, 2021

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:02:52.758084Z

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Observation 6bc9e068-bb37-462c-822a-dbb09901535a · outbound

This paper cites A simple framework for contrastive learning of visual representations, 2020.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives A simple framework for contrastive learning of visual representations, 2020

Reference 6

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Observation 22e4b90d-7775-43a3-b18d-8117bd833b52 · outbound

This paper cites An empirical study of training self-supervised vision transformers, 2021.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives An empirical study of training self-supervised vision transformers, 2021

Reference 7

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Observation 974e7d1b-fbcf-4b51-8afc-5df5a2b52c90 · outbound

This paper cites When vision transformers outperform resnets without pre-training or strong data augmentations, 2022.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives When vision transformers outperform resnets without pre-training or strong data augmentations, 2022

Reference 8

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Observation d607a86c-76d2-4e9a-8c27-3698e0ad753a · outbound

This paper cites Li, and Li Fei-Fei.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives Li, and Li Fei-Fei

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:02:52.566936Z

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Observation cb91acf9-0d52-401b-bd20-efb2d62f8e18 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale, 2021.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives An image is worth 16x16 words: Transformers for image recognition at scale, 2021

Reference 10

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Observation 15d4a801-6c9f-409c-b7d1-45453967c18f · outbound

This paper cites A review on discriminative self-supervised learning methods, 2024.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives A review on discriminative self-supervised learning methods, 2024

Reference 11

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Observation d589fe28-c940-415c-b5e6-d860464cb04b · outbound

This paper cites Synco: Synthetic hard negatives in contrastive learning for better unsupervised visual representations, 2024.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives Synco: Synthetic hard negatives in contrastive learning for better unsupervised visual representations, 2024

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:02:52.343787Z

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Observation 743f9d65-562a-4014-acb8-a95297b3e840 · outbound

This paper cites Richemond, Elena Buchatskaya, Carl Do- ersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Moham- mad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, R´emi Munos, and Michal Valko.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives Richemond, Elena Buchatskaya, Carl Do- ersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Moham- mad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, R´emi Munos, and Michal Valko

Reference 13

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Observation 5ddba3a7-e89b-4a83-92e7-b1e57b1074f6 · outbound

This paper cites Momentum contrast for unsupervised visual repre- sentation learning, 2020.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives Momentum contrast for unsupervised visual repre- sentation learning, 2020

Reference 14

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Observation 6d5fffee-70bb-4b18-b875-267235e7c9a1 · outbound

This paper cites Masked autoencoders are scalable vision learners, 2021.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives Masked autoencoders are scalable vision learners, 2021

Reference 15

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Observation 41fdc6e0-a8ff-4a83-9862-13a3caeba4f0 · outbound

This paper cites Hard negative mixing for contrastive learning, 2020.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives Hard negative mixing for contrastive learning, 2020

Reference 16

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Observation c1ee930a-54f4-4474-9216-0c2fbebc8480 · outbound

This paper cites A style-based generator architecture for generative adversarial networks.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives A style-based generator architecture for generative adversarial networks

Reference 17

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Observation cd9c12ad-0da0-47c8-a437-38d503b07a3c · outbound

This paper cites Self-supervised learning: Generative or contrastive.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives Self-supervised learning: Generative or contrastive

Reference 18

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Observation d1ea7023-c444-44ce-ba87-a0e608cb44b5 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows, 2021.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives Swin transformer: Hierarchical vision transformer using shifted windows, 2021

Reference 19

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Observation 62723b2b-88fd-425c-9d77-4f58f86c3bbc · outbound

This paper cites Synthetic Data for Deep Learning.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives Synthetic Data for Deep Learning

Reference 20

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Observation c905ed95-5269-4a6b-92f6-afeeaca4563a · outbound

This paper cites Fake it till you make it: Learning trans- ferable representations from synthetic imagenet clones.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives Fake it till you make it: Learning trans- ferable representations from synthetic imagenet clones

Reference 21

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Observation 3343c7d2-6231-4a96-9197-a685a2dfdeb3 · outbound

This paper cites Relay diffusion: Unifying diffusion process across resolutions for image syn- thesis.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives Relay diffusion: Unifying diffusion process across resolutions for image syn- thesis

Reference 22

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Observation 5f267c3c-d8de-46bf-8286-dfbd9dc426b8 · outbound

This paper cites Contrastive multiview coding, 2020.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives Contrastive multiview coding, 2020

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:02:51.787956Z

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Observation 0deb7d05-6ff4-4b51-94d9-8e0f5975617f · outbound

This paper cites Learning Vision from Models Rivals Learning Vision from Data.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives Learning Vision from Models Rivals Learning Vision from Data

Reference 24

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Observation 67cd7243-9d3f-4767-8b75-5821c6865826 · outbound

This paper cites Training data-efficient image transformers & distillation through atten- tion, 2021.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives Training data-efficient image transformers & distillation through atten- tion, 2021

Reference 25

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Observation 3a2ca74c-4613-4b3f-ad7b-894b3fefee29 · outbound

This paper cites Repre- sentation learning with contrastive predictive coding, 2019.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives Repre- sentation learning with contrastive predictive coding, 2019

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:02:51.674737Z

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Observation 2851d934-420a-448b-8e60-75122109d61a · outbound

This paper cites Attention is all you need.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives Attention is all you need

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:02:51.605302Z

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Observation c6c2c9fe-f2c0-4380-b5ba-588838c5fa07 · outbound

This paper cites Un- supervised feature learning via non-parametric instance-level discrimination, 2018.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives Un- supervised feature learning via non-parametric instance-level discrimination, 2018

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:02:51.546627Z

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Observation 94132d5f-870d-48bd-bddf-378324cdd83b · outbound

This paper cites Self-supervised learning with swin transformers, 2021.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives Self-supervised learning with swin transformers, 2021

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:02:51.478955Z

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Observation 09a8ef39-daf4-4725-aded-3b263f5e4506 · outbound

This paper cites Decoupled contrastive learning, 2022.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives Decoupled contrastive learning, 2022

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:02:51.399157Z

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Observation f6dd6f3f-1f94-4cb4-838d-501904e19857 · outbound

This paper cites Understanding hard nega- tives in noise contrastive estimation, 2021.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives Understanding hard nega- tives in noise contrastive estimation, 2021

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:02:51.329421Z

Source-reported events for the cited work

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Observation d027f34a-b86a-43d4-820c-f461c80f43d9 · outbound

This paper cites ibot: Image bert pre-training with online tokenizer, 2022.

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives ibot: Image bert pre-training with online tokenizer, 2022

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:02:51.289193Z

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Pith citing papers

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