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.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

43 of 43 outbound references displayed

verified exact1
verified fuzzy20
unresolved22
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:15:57.673952Z

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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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Source-reported events for the cited work

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:15:57.654835Z

Source-reported events for the cited work

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Observation 805c913a-0776-45c5-b369-1cef504f8175 · outbound

This paper cites an unresolved cited work.

Towards Understanding Why Data Augmentation Improves Generalization Unresolved cited work

Reference 4

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Source-reported events for the cited work

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:15:57.635896Z

Source-reported events for the cited work

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Observation 7f3e3807-3db0-401b-b910-78098f42f537 · outbound

This paper cites On mixup regularization.

Towards Understanding Why Data Augmentation Improves Generalization On mixup regularization

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:15:57.623632Z

Source-reported events for the cited work

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

This paper cites an unresolved cited work.

Towards Understanding Why Data Augmentation Improves Generalization Unresolved cited work

Reference 8

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Source-reported events for the cited work

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:15:57.599209Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:15:57.586324Z

Source-reported events for the cited work

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:15:57.573520Z

Source-reported events for the cited work

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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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Source-reported events for the cited work

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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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Source-reported events for the cited work

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Observation 9ae3fdea-159d-4d10-9a7e-2f1a7e06ce50 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-07T23:15:56.820613Z

Source-reported events for the cited work

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This paper cites and Sun, Y.

Towards Understanding Why Data Augmentation Improves Generalization and Sun, Y

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:15:57.553833Z

Source-reported events for the cited work

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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

Resolution: unresolved; no resolver link, observed 2026-08-07T23:15:56.830525Z

Source-reported events for the cited work

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Observation 4cde512c-79d7-4d71-860d-3f00cb8f21c0 · outbound

This paper cites Masked autoencoders are scalable vision learners.

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

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T23:15:56.834570Z

Source-reported events for the cited work

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Observation f3c80d1d-2145-4787-a7c3-ac3743da6412 · outbound

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Towards Understanding Why Data Augmentation Improves Generalization Unresolved cited work

Reference 18

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Source-reported events for the cited work

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:15:57.518021Z

Source-reported events for the cited work

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Observation 666944e7-97c8-474b-897d-d39c9fafb36e · outbound

This paper cites and Li, Y.

Towards Understanding Why Data Augmentation Improves Generalization and Li, Y

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:15:57.506680Z

Source-reported events for the cited work

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Observation ab4eae3b-a26d-471c-8c26-7569067ce5d8 · outbound

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

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

Reference 21

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Source-reported events for the cited work

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Observation 4f3c6ad3-c09c-40c7-9e44-0240218d9896 · outbound

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Towards Understanding Why Data Augmentation Improves Generalization Unresolved cited work

Reference 22

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Source-reported events for the cited work

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

This paper cites H., and Kang, J.

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

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:15:57.475663Z

Source-reported events for the cited work

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

This paper cites and Yuan, Y.

Towards Understanding Why Data Augmentation Improves Generalization and Yuan, Y

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:15:57.462683Z

Source-reported events for the cited work

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Observation 706e738c-c0f7-436e-aaf2-4756b4a3dfa1 · outbound

This paper cites Provable Benefit of Cutout and CutMix for Feature Learning.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:15:57.450345Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:15:57.436423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation acdc67b7-9290-407e-b565-41ec7577cf57 · outbound

This paper cites A., Stimberg, F., Wiles, O., and Mann, T.

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

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:15:57.420165Z

Source-reported events for the cited work

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This paper cites R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D.

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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This paper cites Data augmentation as feature manipulation.

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

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:15:57.401569Z

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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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This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

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

Reference 32

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Source-reported events for the cited work

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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

This paper cites SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:15:57.373337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

This paper cites and Li, Y.

Towards Understanding Why Data Augmentation Improves Generalization and Li, Y

Reference 36

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Source-reported events for the cited work

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Observation 9b4e7eef-c714-4bb3-a288-a08432db95d7 · outbound

This paper cites and Li, Y.

Towards Understanding Why Data Augmentation Improves Generalization and Li, Y

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:15:57.351054Z

Source-reported events for the cited work

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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

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Source-reported events for the cited work

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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

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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

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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

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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

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

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Towards Understanding Why Data Augmentation Improves Generalization write newline

Reference 43

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Pith citing papers

No inbound Pith citation observations are available.