Paper Citation Record · LEDGER

Powerful Design of Small Vision Transformer on CIFAR10

As of 23 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 1 inbound Pith citation observation for arXiv:2501.06220.

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

pith.paper-citation-record.v1
2501.06220 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:58:02.397102Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:01:11.324810Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T20:01:11.661258Z

Reference resolution

28 of 28 outbound references displayed

verified exact1
verified fuzzy18
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3917b2b5-a13e-4bc7-8df5-c2f6354e4967 · outbound

This paper cites SiT: Self-supervised vIsion Transformer.

Powerful Design of Small Vision Transformer on CIFAR10 SiT: Self-supervised vIsion Transformer

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T21:58:02.162977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2027297-5b99-4cd0-96a1-448c43cca708 · outbound

This paper cites an unresolved cited work.

Powerful Design of Small Vision Transformer on CIFAR10 Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:58:03.185853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 690ebda7-4636-4edf-a362-883961575b0c · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

Powerful Design of Small Vision Transformer on CIFAR10 Generating Long Sequences with Sparse Transformers

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T21:58:02.180338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1c35bfc-0e9f-4306-a408-4e0507997d04 · outbound

This paper cites Autoaugment: Learning augmentation strategies from data.

Powerful Design of Small Vision Transformer on CIFAR10 Autoaugment: Learning augmentation strategies from data

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:03.163694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 247c31b3-4484-4dd4-9b25-d0bfe233fda7 · outbound

This paper cites https://github.com/Cydia2018/ViT-cifar10-pruning.

Powerful Design of Small Vision Transformer on CIFAR10 https://github.com/Cydia2018/ViT-cifar10-pruning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:03.137743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b7c307ea-cd7e-4307-a4a7-07792a1dfb8e · outbound

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

Powerful Design of Small Vision Transformer on CIFAR10 An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T21:58:02.202597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77ef276b-4ce6-42b8-98b8-9ba80e1c970c · outbound

This paper cites How to Train Vision Transformer on Small-scale Datasets?.

Powerful Design of Small Vision Transformer on CIFAR10 How to Train Vision Transformer on Small-scale Datasets?

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-10T21:58:02.586447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 03c4aeb8-c6df-476b-b3e8-e33af25509ba · outbound

This paper cites Transformer Feed-Forward Layers Are Key-Value Memories.

Powerful Design of Small Vision Transformer on CIFAR10 Transformer Feed-Forward Layers Are Key-Value Memories

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T21:58:02.223064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b041fe7-a64f-4a4d-af2c-590c58fc75cb · outbound

This paper cites Augment your batch: Improving generalization through instance repetition.

Powerful Design of Small Vision Transformer on CIFAR10 Augment your batch: Improving generalization through instance repetition

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:03.111685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 45fec8cb-61d5-4406-9605-5400dd606b1d · outbound

This paper cites Deep networks with stochastic depth.

Powerful Design of Small Vision Transformer on CIFAR10 Deep networks with stochastic depth

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:03.084781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2382fa95-1b18-4f66-810d-81f8640c0665 · outbound

This paper cites 94% on CIFAR-10 in 3.29 Seconds on a Single GPU.

Powerful Design of Small Vision Transformer on CIFAR10 94% on CIFAR-10 in 3.29 Seconds on a Single GPU

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T21:58:02.243870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee6eff34-9608-4eb9-bfc0-97c687b9f6a0 · outbound

This paper cites Transformers are rnns: Fast autoregressive transformers with linear attention.

Powerful Design of Small Vision Transformer on CIFAR10 Transformers are rnns: Fast autoregressive transformers with linear attention

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:03.063085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ae713bf4-b5ee-44b7-8920-089e78f2db3b · outbound

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

Powerful Design of Small Vision Transformer on CIFAR10 Learning multiple layers of features from tiny images

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T21:58:02.266971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cbc66737-a108-421f-a3be-43bd22820999 · outbound

This paper cites DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model.

Powerful Design of Small Vision Transformer on CIFAR10 DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T21:58:02.272146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c55f2de-d809-4d3f-9b85-31b839779cc9 · outbound

This paper cites Efficient training of visual transformers with small datasets.

Powerful Design of Small Vision Transformer on CIFAR10 Efficient training of visual transformers with small datasets

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:03.020381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f0f5ac6f-68ec-41ef-a427-1e8795a6d582 · outbound

This paper cites Decoupled weight decay regularization, 2019.

Powerful Design of Small Vision Transformer on CIFAR10 Decoupled weight decay regularization, 2019

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:02.985675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 48e54f63-602c-4547-a3be-2d45c57f905b · outbound

This paper cites Vision transformers for dense prediction.

Powerful Design of Small Vision Transformer on CIFAR10 Vision transformers for dense prediction

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:02.961763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5f967c08-0605-4f40-994b-5f684680dd17 · outbound

This paper cites Transformers meet small datasets.

Powerful Design of Small Vision Transformer on CIFAR10 Transformers meet small datasets

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:02.935360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5551544d-db39-4705-a2fb-cf5e2e314d38 · outbound

This paper cites Rethinking the inception architecture for computer vision.

Powerful Design of Small Vision Transformer on CIFAR10 Rethinking the inception architecture for computer vision

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:02.915994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 89eac4a6-8872-480c-abd2-9f64158e1b51 · outbound

This paper cites PyTorch Profiler.

Powerful Design of Small Vision Transformer on CIFAR10 PyTorch Profiler

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:02.889760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 80164fd5-2574-424d-9e8e-12e40911892e · outbound

This paper cites Training data-efficient image transformers & distillation through attention.

Powerful Design of Small Vision Transformer on CIFAR10 Training data-efficient image transformers & distillation through attention

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:02.859908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 85fbdcfb-d022-4139-bf23-beddd4ec237a · outbound

This paper cites Attention is all you need.

Powerful Design of Small Vision Transformer on CIFAR10 Attention is all you need

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:02.830950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f5b20ac8-0a90-4ff6-af9f-413a0465a4de · outbound

This paper cites Mctformer+: Multi-class token transformer for weakly supervised semantic segmentation.

Powerful Design of Small Vision Transformer on CIFAR10 Mctformer+: Multi-class token transformer for weakly supervised semantic segmentation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:02.803975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2b8b4547-62f3-4db0-85f1-ed5582cf38dd · outbound

This paper cites https://github.com/tysam-code/hlb-CIFAR10.

Powerful Design of Small Vision Transformer on CIFAR10 https://github.com/tysam-code/hlb-CIFAR10

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:02.776934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0524756f-a2d4-41b0-b1ae-2a99665cec5c · outbound

This paper cites Cutmix: Regularization strategy to train strong classifiers with localizable features.

Powerful Design of Small Vision Transformer on CIFAR10 Cutmix: Regularization strategy to train strong classifiers with localizable features

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:02.749541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5e80655c-46d5-4496-9869-ef3f4fa2e9c4 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Powerful Design of Small Vision Transformer on CIFAR10 mixup: Beyond Empirical Risk Minimization

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T21:58:02.376979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6bf8749-74d2-443c-bcea-89632e5b852b · outbound

This paper cites Depth-wise convolutions in vision transformers for efficient training on small datasets.

Powerful Design of Small Vision Transformer on CIFAR10 Depth-wise convolutions in vision transformers for efficient training on small datasets

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:02.713230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ac64f4cf-3849-48d5-b32a-35139768d5de · outbound

This paper cites Random erasing data augmentation.

Powerful Design of Small Vision Transformer on CIFAR10 Random erasing data augmentation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:58:02.686919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

Observation 7932ed09-95d6-4f2d-bfdf-5b7efe4231f0 · inbound

Exploring Kolmogorov-Arnold Network Expansions in Vision Transformers for Mitigating Catastrophic Forgetting in Continual Learning cites this paper.

Exploring Kolmogorov-Arnold Network Expansions in Vision Transformers for Mitigating Catastrophic Forgetting in Continual Learning Powerful Design of Small Vision Transformer on CIFAR10

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:01:11.664905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.