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Paper Citation Record · LEDGER

Deeper Inside Deep ViT

As of 19 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2508.04181.

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

pith.paper-citation-record.v1
2508.04181 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:55:21.245290Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

32 of 32 outbound references displayed

  • verified exact3
  • verified fuzzy12
  • unresolved17
  • parse uncertain0
  • malformed identifier0
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External citation measurements

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Outbound references

Observation ce9b271e-cd5b-4d9d-940e-c775af7487cb · outbound

This paper cites Layer Normalization.

Deeper Inside Deep ViT Layer Normalization

Reference 1

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Observation d537ff11-9f77-447a-afdb-510a52e6cf7a · outbound

This paper cites Relational inductive biases, deep learning, and graph networks.

Deeper Inside Deep ViT Relational inductive biases, deep learning, and graph networks

Reference 2

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Observation 4f6f39e6-7e90-44b9-ad55-4f8a6e01e562 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Deeper Inside Deep ViT On the Opportunities and Risks of Foundation Models

Reference 3

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Observation aa14b0b7-5eeb-4708-b860-50af89a7d2fb · outbound

This paper cites Generative Adversarial U-Net for Domain-free Medical Image Augmentation.

Deeper Inside Deep ViT Generative Adversarial U-Net for Domain-free Medical Image Augmentation

Reference 4

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local_arxiv, observed 2026-08-06T00:55:21.739413Z

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Observation a5a602c9-a8e9-49a9-9edf-510b727b5714 · outbound

This paper cites Palm: Scaling language modeling with pathways.

Deeper Inside Deep ViT Palm: Scaling language modeling with pathways

Reference 5

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Observation 01f3bddd-061e-4613-a21c-1a5084f7a61b · outbound

This paper cites Scaling vision transformers to 22 billion parameters.

Deeper Inside Deep ViT Scaling vision transformers to 22 billion parameters

Reference 6

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Observation 83ed8d73-6c15-47e0-b8a7-f963ee453f4a · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Deeper Inside Deep ViT An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 7

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Observation cf9adae1-dc56-48e0-8c76-3ba137bf0695 · outbound

This paper cites Convit: Improving vision transformers with soft convolutional inductive biases.

Deeper Inside Deep ViT Convit: Improving vision transformers with soft convolutional inductive biases

Reference 8

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation c4cc6f83-8ac1-4b2c-8a5d-0ae99cbf394a · outbound

This paper cites Taming transformers for high-resolution image synthesis.

Deeper Inside Deep ViT Taming transformers for high-resolution image synthesis

Reference 9

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Observation 7f89c464-df8a-4d16-abb7-8ad516a64c4e · outbound

This paper cites Se (3)-transformers: 3d roto-translation equivariant attention networks.

Deeper Inside Deep ViT Se (3)-transformers: 3d roto-translation equivariant attention networks

Reference 10

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 5198433f-80c1-47f4-95ca-d5f9a2595d80 · outbound

This paper cites Intriguing properties of transformer training instabilities, 2023.

Deeper Inside Deep ViT Intriguing properties of transformer training instabilities, 2023

Reference 11

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 8f7ce89c-cd89-4e00-a211-f16bec62d09e · outbound

This paper cites Image-to-image translation with conditional adversarial networks.

Deeper Inside Deep ViT Image-to-image translation with conditional adversarial networks

Reference 12

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Observation 3e82dea6-48d0-4aef-9c87-6c0f13d4f520 · outbound

This paper cites TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up.

Deeper Inside Deep ViT TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up

Reference 13

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local_arxiv, observed 2026-08-06T00:55:21.608433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 726ddafc-e74b-4468-be06-2de580ab8233 · outbound

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

Deeper Inside Deep ViT A style-based generator architecture for generative adversarial networks

Reference 14

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Observation 169df59b-0623-41ff-bc80-a22425c70811 · outbound

This paper cites ViTGAN: Training GANs with Vision Transformers.

Deeper Inside Deep ViT ViTGAN: Training GANs with Vision Transformers

Reference 15

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Observation beac8d87-91cb-4fa4-9755-d9e8af67c31e · outbound

This paper cites Blendgan: Implicitly gan blending for arbitrary stylized face generation.

Deeper Inside Deep ViT Blendgan: Implicitly gan blending for arbitrary stylized face generation

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation ce9e0072-cd6b-4b34-ac2d-fdcc3c05310f · outbound

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

Deeper Inside Deep ViT Swin transformer: Hierarchical vision transformer using shifted windows

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 7ad5847d-0512-4625-8276-800f621c1cb1 · outbound

This paper cites Decoupled Weight Decay Regularization.

Deeper Inside Deep ViT Decoupled Weight Decay Regularization

Reference 18

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Observation fbbc5d8e-e7ea-42c4-a938-fc6ba77c85c9 · outbound

This paper cites Large Scale Transfer Learning for Differentially Private Image Classification.

Deeper Inside Deep ViT Large Scale Transfer Learning for Differentially Private Image Classification

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation cc19d6e6-265f-4cb9-b6af-4b1e2b37b197 · outbound

This paper cites MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer.

Deeper Inside Deep ViT MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer

Reference 20

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Observation 1a83d089-c7e7-4221-bfd1-2f50f44f79ff · outbound

This paper cites Mixed Precision Training.

Deeper Inside Deep ViT Mixed Precision Training

Reference 21

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Observation 0c51c60c-4f8b-4363-b115-da709a5188fb · outbound

This paper cites Foundation models for generalist medical artificial intelligence.

Deeper Inside Deep ViT Foundation models for generalist medical artificial intelligence

Reference 22

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation bb34ee32-9dc7-46d1-88f1-877909059180 · outbound

This paper cites Do vision transformers see like convolutional neural networks? Advances in Neural Information Processing Systems, 34:12116–12128, 2021.

Deeper Inside Deep ViT Do vision transformers see like convolutional neural networks? Advances in Neural Information Processing Systems, 34:12116–12128, 2021

Reference 23

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Observation 2b6bbd18-ad90-4c93-b1aa-46c05470fa30 · outbound

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

Deeper Inside Deep ViT Zero-shot text-to-image generation

Reference 24

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Observation 4c35ea16-3187-47c9-b043-fd16e546e1b9 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

Deeper Inside Deep ViT U-net: Convolutional networks for biomedical image segmentation

Reference 25

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Observation b0782635-139e-4750-a0c0-20320b789cdf · outbound

This paper cites Revisiting unreasonable effectiveness of data in deep learning era.

Deeper Inside Deep ViT Revisiting unreasonable effectiveness of data in deep learning era

Reference 26

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Source-reported events for the cited work

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Observation a1214035-30c1-4599-bb67-5c1117dd3ba1 · outbound

This paper cites LaMDA: Language Models for Dialog Applications.

Deeper Inside Deep ViT LaMDA: Language Models for Dialog Applications

Reference 27

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Observation 06a9e207-c09c-46d8-b923-d7710f77a39c · outbound

This paper cites Attention is all you need.

Deeper Inside Deep ViT Attention is all you need

Reference 28

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Observation 1cccc6d1-1277-40ba-b87f-2668fc5630f5 · outbound

This paper cites Internimage: Exploring large-scale vision foundation models with deformable convolutions.

Deeper Inside Deep ViT Internimage: Exploring large-scale vision foundation models with deformable convolutions

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation f2b1d65f-8e30-4f13-a132-e64a23688d9f · outbound

This paper cites Generative adversarial network in medical imaging: A review.

Deeper Inside Deep ViT Generative adversarial network in medical imaging: A review

Reference 30

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Observation cf77a910-d2e1-4b42-92c6-fc9dc3cfe06d · outbound

This paper cites Scaling vision transform- ers.

Deeper Inside Deep ViT Scaling vision transform- ers

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation b8e52cf8-5959-459d-84a8-1ad36ba0e74c · outbound

This paper cites Unpaired image-to-image translation using cycle-consistent adversarial networks.

Deeper Inside Deep ViT Unpaired image-to-image translation using cycle-consistent adversarial networks

Reference 32

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Pith citing papers

No inbound Pith citation observations are available.