Pith. sign in

Paper Citation Record · LEDGER

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization

As of 21 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2507.04302.

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

pith.paper-citation-record.v1
2507.04302 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:56:12.010013Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

51 of 51 outbound references displayed

  • verified exact0
  • verified fuzzy37
  • unresolved14
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ff0d0ff4-1b37-42f9-915d-c7d8de952f5c · outbound

This paper cites Chaos: an introduction to dynamical systems.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Chaos: an introduction to dynamical systems

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:14.247542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:05.819613Z digest=sha256:040c60d3bde629cf05933fce5cf1caecae1f1072ae358475150b2beaca79aef2

Observation a76cab79-0ad8-4790-91f0-2a96996b5aae · outbound

This paper cites Adversarial bayesian augmentation for single-source domain generaliza- tion.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Adversarial bayesian augmentation for single-source domain generaliza- tion

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:14.229607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:05.949288Z digest=sha256:0f69fcbcca19d365551c1b002ab5da047a0b7cc383287f2526b25c14772003df

Observation 1032ede5-d917-4970-8625-75408a8cce66 · outbound

This paper cites Randaugment: Practical automated data augmenta- tion with a reduced search space.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Randaugment: Practical automated data augmenta- tion with a reduced search space

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:14.196764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:06.075213Z digest=sha256:db3741f29d4199e69993cd0f9f28c71e57b7bfa055b1c6aad54ddac8d7a5c5a3

Observation 2d84ab21-8564-4c55-bd42-4c3b7e708d7c · outbound

This paper cites Attention consistency on visual corruptions for single-source domain generalization.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Attention consistency on visual corruptions for single-source domain generalization

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:14.136152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:06.174246Z digest=sha256:48b536cfcf6082fce47f878c3dc1aa53a8b8277b8f0786eec4a0c8ff730cc056

Observation 6cf41ec0-2cef-4593-9dd3-46420c7e641f · outbound

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

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Improved Regularization of Convolutional Neural Networks with Cutout

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-06T19:56:06.285240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:56:06.285240Z digest=sha256:db6cc563854fd535c0d5d7812759d26455e333c0a4bec009dfcedc3fd4a42337

Observation a32a8bdd-ab3e-42ba-bbdb-958f39fbcadd · outbound

This paper cites Optimal Machine Intelligence at the Edge of Chaos.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Optimal Machine Intelligence at the Edge of Chaos

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T19:56:06.389727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:56:06.389727Z digest=sha256:6998a23d3782a4f23d3beb203d68877fb438e3ec7ed799d0a3348aa1465ac219

Observation 2ad24fda-a7b2-4227-96ea-cf04c2d55084 · outbound

This paper cites Sharpness-Aware Minimization for Efficiently Improving Generalization.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Sharpness-Aware Minimization for Efficiently Improving Generalization

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-06T19:56:06.459138Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:56:06.459138Z digest=sha256:965d94d2dbaf633f8b7e4994dd10bcea2cc1cf578ae0524d226b71610a2c7872

Observation 97991a22-64af-4ffc-85b2-9391c95b5939 · outbound

This paper cites Lya- punov stable learning laws for multilayer recurrent neural networks.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Lya- punov stable learning laws for multilayer recurrent neural networks

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:14.119662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:06.581689Z digest=sha256:dfa2c237809bc4911c703499dbe16ab382b6ad250af05b40f4f6324c6f4b94fb

Observation 6117d8b0-422e-4ef0-853a-733b6c17f88f · outbound

This paper cites Train faster, generalize better: Stability of stochastic gradient descent.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Train faster, generalize better: Stability of stochastic gradient descent

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:14.091712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:06.726785Z digest=sha256:4a47d0f018aa1d5658f3cb0283b60a4cf879830940878aa791c235d32342e75f

Observation d290bcb1-8ba7-4963-b715-9598b2898c1c · outbound

This paper cites Chaotic nature of the electroencephalo- gram during shallow and deep anesthesia: From analysis of the lyapunov exponent.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Chaotic nature of the electroencephalo- gram during shallow and deep anesthesia: From analysis of the lyapunov exponent

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:14.046412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:06.821603Z digest=sha256:9c0f347340ff861aa6fbdb87ba780c155981184d76fe069ef1601b01a8c0d6bb

Observation a6b63ecf-3ab9-4ca5-a591-5e0d4c111f3d · outbound

This paper cites Deep residual learning for image recognition.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Deep residual learning for image recognition

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:14.024512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:06.952309Z digest=sha256:f1abd520ee7849e2f3e7bf21ff6b2fe07830d1d5f700e398c5432f7440a45328

Observation c396c754-6210-47db-99a5-a55d3bb11c98 · outbound

This paper cites AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-06T19:56:07.077598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:56:07.077598Z digest=sha256:d5de8fdfcdb5ebacda7b8cd7d208e6673a613894555c593fc91a312113209a3a

Observation c7079e36-defd-468e-ac9b-a2584dd542ec · outbound

This paper cites Self-challenging improves cross-domain generalization.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Self-challenging improves cross-domain generalization

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.974732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:07.225283Z digest=sha256:1769f4d000a9c5a7598d8c27e6b9c3a969f543902a687ab022ca1c4807b7be60

Observation f9157860-c61f-4f79-a956-d93808644278 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Adam: A Method for Stochastic Optimization

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-06T19:56:07.340227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:56:07.340227Z digest=sha256:3284c7a0fefadd098f4b6a7368ab77ee712a892632ea18b4bfeb3f7375130b75

Observation 920b7b01-4454-4cd8-91fc-90a64358e315 · outbound

This paper cites Deeper, broader and artier domain generaliza- tion.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Deeper, broader and artier domain generaliza- tion

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.927187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:07.431645Z digest=sha256:f4a2b5ec7d0864bcf7d32d8dcbc6ed444ebaf67a2f70dbe01be6755c63d42854

Observation 6cd60369-8977-4495-a8e2-5e2d42f8b3da · outbound

This paper cites Pro- gressive domain expansion network for single domain gen- eralization.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Pro- gressive domain expansion network for single domain gen- eralization

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.895121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:07.511959Z digest=sha256:5c6fe5491b6286a49d4399ea2b442a111de7657f24b6721fe9d1dbe2a443ff7c

Observation fd77126c-4054-4dde-98f5-afee9da6ed4c · outbound

This paper cites Deep learning via dynamical systems: An approximation perspective.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Deep learning via dynamical systems: An approximation perspective

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.851449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:07.579551Z digest=sha256:5f8c8ef67d3a565b60dec5f0dedd34ae13d8bbe1999644a3908316bd50f6694b

Observation d12206ff-734c-46f4-a8c5-8ba39f1e05c8 · outbound

This paper cites Deep learning for hy- perspectral image classification: An overview.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Deep learning for hy- perspectral image classification: An overview

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.822218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:07.690805Z digest=sha256:ad6582521ac4d32f859cbc30f0d4930123a6a9cb0dde84c0cfd8fc3002c37a0a

Observation 0c3ed359-a1eb-4584-b0f0-d51218974b1d · outbound

This paper cites Deep Learning Theory Review: An Optimal Control and Dynamical Systems Perspective.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Deep Learning Theory Review: An Optimal Control and Dynamical Systems Perspective

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-06T19:56:07.846491Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:56:07.846491Z digest=sha256:1f53840b9e0cd6f9f791c82e94c27652e7b3c31416b907a08a5b61cf39f89129

Observation 468ccfb1-3870-4ba5-a323-7c8162a59b66 · outbound

This paper cites Decoupled Weight Decay Regularization.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Decoupled Weight Decay Regularization

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-06T19:56:08.054651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:56:08.054651Z digest=sha256:0e1e1ce710054a3130fd2e07b6dd51ca24a977c48aa3f1d6dbb2a6edde489a78

Observation 805aff57-373e-409d-b8c6-d6c3badc57fe · outbound

This paper cites Reducing domain gap by reduc- ing style bias.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Reducing domain gap by reduc- ing style bias

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.783751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:08.302158Z digest=sha256:a9f8e5add64b8eae52cf29230e4fb0cb5a3d134ae381941a71749bf918e72a5d

Observation e19c7144-3d19-4b73-a338-33a33ce9f58f · outbound

This paper cites A method for solving the convex program- ming problem with convergence rate o (1/k2).

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization A method for solving the convex program- ming problem with convergence rate o (1/k2)

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.759765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:08.446517Z digest=sha256:1c6f0953153d028090880da1fb9dab40f4e53ad6c705fadd6049307823c306c1

Observation fb8507e8-5f73-426f-97a9-0bc7e6a940e6 · outbound

This paper cites A survey on transfer learn- ing.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization A survey on transfer learn- ing

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.720393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:08.682791Z digest=sha256:467c0c4a79ba218fe076ee2eba1de6be5777f4603733822232f0cfd8c54e3cec

Observation 5745c5a2-36d6-4b64-a27c-46a88f771406 · outbound

This paper cites Moment matching for multi-source domain adaptation.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Moment matching for multi-source domain adaptation

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-06T19:56:08.762639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:56:08.762639Z digest=sha256:c76fb0ce36e90a772e1ae25fc2c00da69f75a97516cea7ad97dab4816b5076e0

Observation f0435392-1005-4b0a-bc46-768e574aaefc · outbound

This paper cites Learning to learn single domain generalization.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Learning to learn single domain generalization

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.658295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:08.870347Z digest=sha256:99c795b9c2d8c42b4cea63e21dae6c18f60edfe49e74d676d80ca5c75cf2f1ef

Observation 7ed9339a-7d68-40de-a526-cec5cd816a32 · outbound

This paper cites A stochastic approxi- mation method.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization A stochastic approxi- mation method

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.623242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:09.014838Z digest=sha256:3d5f4bfd0dfd10db497f27f9685f7bab1cf44de7af07e766ad13c8ecc6ba5fec

Observation 0474882e-5812-41cc-b317-c9f98724fac6 · outbound

This paper cites Transfer learning for visual categorization: A survey.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Transfer learning for visual categorization: A survey

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.591344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:09.171631Z digest=sha256:bcb2b5352160cedf10f118d6c59dbb271f5c4f53263c1e6abfa17d160940c6aa

Observation b111f3a3-98ba-4933-86fd-a53da4b13a30 · outbound

This paper cites Gradient Matching for Domain Generalization.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Gradient Matching for Domain Generalization

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-06T19:56:09.328287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:56:09.328287Z digest=sha256:1962cbd7252ffa0d10e46149b8adede3ae7fbb956e88fad611e6183778d344af

Observation 73251f2b-ee84-431f-8ce5-880ad7722412 · outbound

This paper cites Introduction to focus issue: When machine learn- ing meets complex systems: Networks, chaos, and nonlinear dynamics.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Introduction to focus issue: When machine learn- ing meets complex systems: Networks, chaos, and nonlinear dynamics

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.554886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:09.463732Z digest=sha256:adf2ce4aeb385c02a640097916ecc2b35e84afc7947b6380434b6804cd2cda7f

Observation 1f30fe5a-c7f2-42ff-86c9-4181387ad9a7 · outbound

This paper cites Rmsprop: Divide the gradient by a running average of its recent magnitude.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Rmsprop: Divide the gradient by a running average of its recent magnitude

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.484088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:09.532143Z digest=sha256:93758985fa049d13d735895e4c686276de74d0e8ecb118368ecfa58b8ed14632

Observation 21624dd9-e708-43f6-b80a-25c8dc599d69 · outbound

This paper cites Deep hashing network for unsupervised domain adaptation.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Deep hashing network for unsupervised domain adaptation

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.443377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:09.657437Z digest=sha256:0306488487cfa997561b904c4241fff88af0dc2f15b2747d5d59f4a631d4ebd2

Observation 0439a3b0-4983-439b-bb18-441ee116ac0d · outbound

This paper cites On lyapunov exponents for rnns: Under- standing information propagation using dynamical systems tools.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization On lyapunov exponents for rnns: Under- standing information propagation using dynamical systems tools

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.414516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:09.811349Z digest=sha256:5d77ba41e17d0a3e96ee679a297ce527dc7ef7a033d2be936719dde41f447420

Observation 29412431-203b-41cb-a9bc-cf96f8b16e9a · outbound

This paper cites Generalizing to unseen domains via adversarial data augmentation.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Generalizing to unseen domains via adversarial data augmentation

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.382763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:09.975475Z digest=sha256:d1d800a6a3b84fcd924050740c1146eac851701a41bc098375a9055656f9757a

Observation d5d1f4b7-ff1e-4650-83f6-48bc7ee0d8f4 · outbound

This paper cites Meta convolutional neural networks for single domain generalization.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Meta convolutional neural networks for single domain generalization

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.348687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:10.156856Z digest=sha256:947c8e151c6f8ce65fc1867fb835b91ce0da4b18c573aca0ca178af633a497c9

Observation 46dde176-a00f-44d1-8d3d-f2122f6f539c · outbound

This paper cites Sharpness-aware gradient matching for domain generaliza- tion.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Sharpness-aware gradient matching for domain generaliza- tion

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-06T19:56:10.292956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:56:10.292956Z digest=sha256:4d5333be14b881c2aa54808c44ecc0f8dc5913c8c358ec05735157307c169eac

Observation 944d8ea9-0738-4225-b7e8-17422c0337b1 · outbound

This paper cites Learning to diversify for single do- main generalization.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Learning to diversify for single do- main generalization

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.303580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:10.366064Z digest=sha256:eb89f0367281d46752b344b1bc856cea39ba1cb2eb0b7f01a546e34b2cfb7e2b

Observation f2f31b99-99a6-4f7a-924f-aae2325dbe91 · outbound

This paper cites Simde: A simple domain expan- sion approach for single-source domain generalization.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Simde: A simple domain expan- sion approach for single-source domain generalization

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.282014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:10.450226Z digest=sha256:8ab9a5a5b12602441147f4f63fa0b8ee1cd7b7a982d130e21f385fbec286207f

Observation 0a28390d-8f7c-4cb4-87b6-6353e4008bc0 · outbound

This paper cites Robust and Generalizable Visual Representation Learning via Random Convolutions.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Robust and Generalizable Visual Representation Learning via Random Convolutions

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T19:56:10.540960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:56:10.540960Z digest=sha256:bf3c73eb11e701a4c556db7ac00ab993252f26466e2665806556e559994d1f82

Observation 878abe0f-2aa0-4259-a7fe-0406cd193c44 · outbound

This paper cites Improve Unsupervised Domain Adaptation with Mixup Training.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Improve Unsupervised Domain Adaptation with Mixup Training

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-06T19:56:10.650840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:56:10.650840Z digest=sha256:b608269933a7fbe0c718c98741541a9377c40606694c643239b4a78b5474f6e2

Observation 97a8650a-4ff1-4132-b754-ef5a8a536775 · outbound

This paper cites Causality- inspired domain expansion network for single domain gener- alization.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Causality- inspired domain expansion network for single domain gener- alization

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.250377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:10.768324Z digest=sha256:aadeefbfb071f76de8230d3aea37de768dba9c46efa8977f912e8bb00127a827

Observation 3afc86e7-24b9-475d-8ec2-6ea683dd2e4a · outbound

This paper cites Practical single domain generalization via training-time and test-time learn- ing.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Practical single domain generalization via training-time and test-time learn- ing

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.211091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:10.899484Z digest=sha256:134e9157b3be53aa7a02f6c6e6c0f3552daa73f3e075a21b603a2ff92ef0e56c

Observation 35c07eaf-527b-4ef2-a6ea-27236fa1d7ab · outbound

This paper cites Gradient surgery for multi-task learning.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Gradient surgery for multi-task learning

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.160768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:10.971095Z digest=sha256:95438fb8ea72080224151a477464dfed3e7714f830f181f4611e2af7a1e8e663

Observation b8a96b27-a357-4abb-9881-ed1ab9cdc94b · outbound

This paper cites Generalizing deep learning for medical image segmentation to unseen do- mains via deep stacked transformation.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Generalizing deep learning for medical image segmentation to unseen do- mains via deep stacked transformation

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.137896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:11.081589Z digest=sha256:7102b857c7fbd2e432ca9775232bbc9d9ab79672160f6e43d2048c96f6d01c49

Observation a99ab781-1148-405f-afaa-9b17f006cede · outbound

This paper cites Edge of chaos as a guiding principle for modern neural network training.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Edge of chaos as a guiding principle for modern neural network training

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-06T19:56:11.207577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:56:11.207577Z digest=sha256:bf828cfa068597b3de4aec619cbfe752eb5b9afe358397828f869e7ebd52fd42

Observation 50e38495-c65e-4841-8a39-eb37f602cdab · outbound

This paper cites Asymptotic edge of chaos as guiding principle for neural net- work training.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Asymptotic edge of chaos as guiding principle for neural net- work training

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.106299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:11.334692Z digest=sha256:97e3795817473bdd817a771f39983bd02d365f7279ffd022ad4ddf51eefc2100

Observation b70307c5-b7aa-435f-b3d3-f4a1088c9a60 · outbound

This paper cites Flatness-aware minimization for domain generalization.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Flatness-aware minimization for domain generalization

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.066219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:11.443564Z digest=sha256:6bc281592f7b444fe8b290bfff1e4818fe14f19719de0347cfee61c7556d1c5e

Observation 9f4f0cd4-2c4b-4da2-95b2-fd3b49ebd054 · outbound

This paper cites Maximum-entropy adversarial data augmentation for im- proved generalization and robustness.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Maximum-entropy adversarial data augmentation for im- proved generalization and robustness

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.036531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:11.561421Z digest=sha256:daf436a01c1fe490e4d0d7c4e28d7a343d4472a6202fe29864c5c4de868f1d38

Observation 0f953cc0-3569-44a8-baba-64fea25e883d · outbound

This paper cites Advst: Revisiting data augmentations for single domain generaliza- tion.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Advst: Revisiting data augmentations for single domain generaliza- tion

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:13.009227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:11.664495Z digest=sha256:2e0b21c47eed595a11ae2631f78a61f0af9a46f854f3b373b0ab01f2785cbf93

Observation 5a65a37d-f414-4eb9-b811-07366c7ffb95 · outbound

This paper cites Mixstyle neural networks for domain generalization and adaptation.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Mixstyle neural networks for domain generalization and adaptation

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:12.922732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:11.804165Z digest=sha256:5ee5fda3ce82b3ba9ec712ae6a300df7888e45476c011f2d79446c153fdbfdc4

Observation 8a785e04-f8e3-40ce-ac46-07b86e7619d5 · outbound

This paper cites A comprehensive survey on transfer learning.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization A comprehensive survey on transfer learning

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:56:12.624282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:56:11.915985Z digest=sha256:1ed0b49ba8cbe3ff15b8e9823bb5c9ae06fd10d9f720467c5a8ba4453069cd2e

Observation ab0ee24f-7e30-46b1-aa85-b5ce7b570465 · outbound

This paper cites Surrogate Gap Minimization Improves Sharpness-Aware Training.

Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization Surrogate Gap Minimization Improves Sharpness-Aware Training

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-06T19:56:12.010013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:56:12.010013Z digest=sha256:2939296b417c145c3147ea167a8267cd459807aa744147dfb3f4a8afc85f9312

Pith citing papers

No inbound Pith citation observations are available.