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Paper Citation Record · LEDGER

Towards Understanding Why Data Augmentation Improves Generalization

As of 8 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2502.08940.

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

pith.paper-citation-record.v1
2502.08940 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T23:15:57.156112Z

measured 43 of 43 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

43 of 43 outbound references displayed

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External citation measurements

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Outbound references

Observation 0918301c-84a7-4ca7-976c-5829ee91cb45 · outbound

This paper cites and Li, Y.

Towards Understanding Why Data Augmentation Improves Generalization and Li, Y

Reference 1

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Observation ed762ca8-c5b7-411c-9bb2-40edcb80d8a3 · outbound

This paper cites and Li, Y.

Towards Understanding Why Data Augmentation Improves Generalization and Li, Y

Reference 2

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Observation 306511db-20a2-4ff8-9554-a12bf4f983ba · outbound

This paper cites Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks.

Towards Understanding Why Data Augmentation Improves Generalization Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks

Reference 3

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Towards Understanding Why Data Augmentation Improves Generalization Unresolved cited work

Reference 4

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Observation 762becf4-b815-4c12-aa96-5a0114d2e8be · outbound

This paper cites Language Models are Few-Shot Learners.

Towards Understanding Why Data Augmentation Improves Generalization Language Models are Few-Shot Learners

Reference 5

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Observation b838ce57-6609-4079-ba23-170b37117eb6 · outbound

This paper cites Benign overfitting in two-layer convolutional neural networks.

Towards Understanding Why Data Augmentation Improves Generalization Benign overfitting in two-layer convolutional neural networks

Reference 6

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This paper cites On mixup regularization.

Towards Understanding Why Data Augmentation Improves Generalization On mixup regularization

Reference 7

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Observation 9a760909-8cd0-45af-ac2a-30ebc92d8329 · outbound

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Towards Understanding Why Data Augmentation Improves Generalization Unresolved cited work

Reference 8

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Observation 1a329b94-09a5-4ac4-9533-4d47454ba7c9 · outbound

This paper cites Why does sharpness-aware minimization generalize better than sgd? Advances in neural information processing systems, 36, 2024.

Towards Understanding Why Data Augmentation Improves Generalization Why does sharpness-aware minimization generalize better than sgd? Advances in neural information processing systems, 36, 2024

Reference 9

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Observation da73878c-9bbf-49fd-bb38-e76e344923dc · outbound

This paper cites Provably learning diverse features in multi-view data with midpoint mixup.

Towards Understanding Why Data Augmentation Improves Generalization Provably learning diverse features in multi-view data with midpoint mixup

Reference 10

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Observation c149bf23-ce83-45e8-a2b5-dc164762b1a5 · outbound

This paper cites A kernel theory of modern data augmentation.

Towards Understanding Why Data Augmentation Improves Generalization A kernel theory of modern data augmentation

Reference 11

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Observation 157f992f-74b9-47ac-888a-952959c1c7ac · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Towards Understanding Why Data Augmentation Improves Generalization Imagenet: A large-scale hierarchical image database

Reference 12

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Observation d3c7aeb3-8c62-4085-8eb9-1739c1668352 · outbound

This paper cites Improved Regularization of Convolutional Neural Networks with Cutout.

Towards Understanding Why Data Augmentation Improves Generalization Improved Regularization of Convolutional Neural Networks with Cutout

Reference 13

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

Towards Understanding Why Data Augmentation Improves Generalization An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 14

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Observation 4b0687b9-7ca0-4287-b988-df65862db6fe · outbound

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Towards Understanding Why Data Augmentation Improves Generalization and Sun, Y

Reference 15

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Observation 4e666a2d-6156-4e1d-b5d0-54fad59d0f85 · outbound

This paper cites Deep residual learning for image recognition.

Towards Understanding Why Data Augmentation Improves Generalization Deep residual learning for image recognition

Reference 16

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Towards Understanding Why Data Augmentation Improves Generalization Masked autoencoders are scalable vision learners

Reference 17

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Towards Understanding Why Data Augmentation Improves Generalization Unresolved cited work

Reference 18

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Observation 87ac86fc-8659-4515-b328-e39cd9053506 · outbound

This paper cites Understanding convergence and generalization in federated learning through feature learning theory.

Towards Understanding Why Data Augmentation Improves Generalization Understanding convergence and generalization in federated learning through feature learning theory

Reference 19

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Towards Understanding Why Data Augmentation Improves Generalization and Li, Y

Reference 20

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Towards Understanding Why Data Augmentation Improves Generalization Learning multiple layers of features from tiny images

Reference 21

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Towards Understanding Why Data Augmentation Improves Generalization Unresolved cited work

Reference 22

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Observation bf0b4a37-18b5-42bd-9c99-70b0b9540d7a · outbound

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Towards Understanding Why Data Augmentation Improves Generalization H., and Kang, J

Reference 23

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Observation 8e5523ab-738e-4ce5-9ae4-95f4ccacfd69 · outbound

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Towards Understanding Why Data Augmentation Improves Generalization and Yuan, Y

Reference 24

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Towards Understanding Why Data Augmentation Improves Generalization Provable Benefit of Cutout and CutMix for Feature Learning

Reference 25

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Observation 094c5727-7ff6-421f-aa76-117eeaff5809 · outbound

This paper cites A unified analysis of mixed sample data augmentation: A loss function perspective.

Towards Understanding Why Data Augmentation Improves Generalization A unified analysis of mixed sample data augmentation: A loss function perspective

Reference 26

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Observation 6feda57f-5e2a-44e8-8a3d-3ce2daaafa78 · outbound

This paper cites Does data augmentation lead to positive margin? In International Conference on Machine Learning, pp.\ 5321--5330.

Towards Understanding Why Data Augmentation Improves Generalization Does data augmentation lead to positive margin? In International Conference on Machine Learning, pp.\ 5321--5330

Reference 27

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Towards Understanding Why Data Augmentation Improves Generalization A., Stimberg, F., Wiles, O., and Mann, T

Reference 28

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Source-reported events for the cited work

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Towards Understanding Why Data Augmentation Improves Generalization R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D

Reference 29

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Observation 80a20dde-6ac2-44c5-9cfe-c66d416ec167 · outbound

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Towards Understanding Why Data Augmentation Improves Generalization Data augmentation as feature manipulation

Reference 30

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Observation 29bdba32-ec8d-4656-a601-710c06c8a8aa · outbound

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Towards Understanding Why Data Augmentation Improves Generalization and Khoshgoftaar, T

Reference 31

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Towards Understanding Why Data Augmentation Improves Generalization Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 32

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Towards Understanding Why Data Augmentation Improves Generalization Dropout: a simple way to prevent neural networks from overfitting

Reference 33

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Observation 01710d71-2e90-4185-855f-546cef80f340 · outbound

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Towards Understanding Why Data Augmentation Improves Generalization SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization

Reference 34

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Observation ccf0bbfb-f1a1-409c-8a02-c8b003e092c9 · outbound

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Towards Understanding Why Data Augmentation Improves Generalization C., Murino, V., and Savarese, S

Reference 35

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Source-reported events for the cited work

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Observation 41b1aacc-20c8-45a9-8f0a-b5e62d89cc8d · outbound

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Towards Understanding Why Data Augmentation Improves Generalization and Li, Y

Reference 36

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Towards Understanding Why Data Augmentation Improves Generalization and Li, Y

Reference 37

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 3a7a988e-0e2f-4be4-b8e3-d760461a91bf · outbound

This paper cites J., Chun, S., Choe, J., and Yoo, Y.

Towards Understanding Why Data Augmentation Improves Generalization J., Chun, S., Choe, J., and Yoo, Y

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-07T23:15:57.133926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T23:15:57.133926Z digest=sha256:fe32b778dfc264028e60c3690cc92751f6385c0b7821f79af44a36b903b67a9d

Observation d6f30f52-2955-4fd4-9f4b-7b279b67c71f · outbound

This paper cites Understanding deep learning (still) requires rethinking generalization.

Towards Understanding Why Data Augmentation Improves Generalization Understanding deep learning (still) requires rethinking generalization

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T23:15:57.331341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T23:15:57.138423Z digest=sha256:82a8be6074e509ad492bb8f0187e585d1bd1231234ffc6023ec8315340ca3967

Observation 5dbdd416-3fda-429f-8ef5-870e758317e5 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Towards Understanding Why Data Augmentation Improves Generalization mixup: Beyond Empirical Risk Minimization

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T23:15:57.142901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T23:15:57.142901Z digest=sha256:86a1533d4205c75b6a941a5cb463bd54aa6d1d11456d6182a588cd98b8c74fb4

Observation 479b3b0a-8bfa-4093-bf14-18d7283b97f8 · outbound

This paper cites How Does Mixup Help With Robustness and Generalization?.

Towards Understanding Why Data Augmentation Improves Generalization How Does Mixup Help With Robustness and Generalization?

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-07T23:15:57.147380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T23:15:57.147380Z digest=sha256:cace7de8e5251e8e51cbc5703c42d42988951406a0ffb2bf9cace3c81b368c96

Observation d648a3a5-1ef4-45aa-82f7-8a05dca17ea6 · outbound

This paper cites The benefits of mixup for feature learning.

Towards Understanding Why Data Augmentation Improves Generalization The benefits of mixup for feature learning

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T23:15:57.318074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T23:15:57.152048Z digest=sha256:4ac245d761fd04b6e4fb1be6244d90dffd15f75cc027f409a1c41ee1e6006ee4

Observation 326a36ab-1142-4a83-beae-9a4e6912286e · outbound

This paper cites write newline.

Towards Understanding Why Data Augmentation Improves Generalization write newline

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-07T23:15:57.156112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T23:15:57.156112Z digest=sha256:80ad1f6d706abb765797970cad8a8b334534193a7c0b2c5af97dadb3b6dc4bec

Pith citing papers

No inbound Pith citation observations are available.