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Paper Citation Record · LEDGER

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

As of 9 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2507.04302.

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2507.04302 v1

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measured 51 of 51 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

51 of 51 outbound references displayed

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External citation measurements

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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

Reference 26

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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

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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

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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

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Source-reported events for the cited work

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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

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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

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Source-reported events for the cited work

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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

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Source-reported events for the cited work

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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

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Source-reported events for the cited work

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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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

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

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source=pdf_text observed=2026-08-06T19:56:10.292956Z digest=sha256:29b1cd3f5b62fa04be22566058ddee6ecbd29b600038cdc777a0eb2941984857

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

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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 2026-08-09T06:31:02.800959+00:00.

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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

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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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:56:10.450226Z digest=sha256:62fb4e981a348d93ea19f8a1201bd0e8705a930e94ebde7c622e34c010fbce25

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

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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=sha256:3a0837e0fd447e665c3e67fa89ad20267c34de5531f14e44e8882cd4e2d1d745

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

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:56:10.650840Z digest=sha256:ca44d1a08e7a17bb6525f45bd4cea216a6ff277096616956f194c44f2ece2260

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 2026-08-09T06:31:02.800959+00:00.

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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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:56:10.899484Z digest=sha256:477d1f4a95143f35c76732e868854d7e1a3d230f1a1dc19f1ca445f5328821b6

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 2026-08-09T06:31:02.800959+00:00.

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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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:56:11.081589Z digest=sha256:79e8eff1b5aa8483c47cfdf08f9b0eb7924462f6ed1d1edd7ad5bc6cd82fd92b

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

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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=sha256:1f7fd6e8a14a2243749d42c3fd6d26def7f5fc8a21c69552238d49cc449f6ccb

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:56:11.334692Z digest=sha256:6529caa3a2f7e5682dacb74b2dc16f89ff7cc4b9d5670c440db63d15cc824439

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 2026-08-09T06:31:02.800959+00:00.

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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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:56:11.561421Z digest=sha256:d1277f1c539a137c30d8a24dc5df9d02f7f3a418a2eda8d4ae2a5c5a905c903f

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:56:11.664495Z digest=sha256:ddedff7f0ada743daead30325740b07755929bd442abe38c897481debee054e9

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:56:11.804165Z digest=sha256:392c7c4ab5fb0ef6a80533a9e326535fa6c4bd41c074a57c7cbecc3813aa9ae2

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:56:11.915985Z digest=sha256:a227a695f5b0dab9f8b9bfcd93bb676203c6d02062fca7d01e1131de842fc5f8

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
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers

No inbound Pith citation observations are available.