Paper Citation Record · LEDGER

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation

As of 21 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2505.15194.

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

pith.paper-citation-record.v1
2505.15194 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

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measured 33 of 33 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

33 of 33 outbound references displayed

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External citation measurements

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Outbound references

Observation dc090aad-80a1-493f-a998-531e32b26998 · outbound

This paper cites Deep learning.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Deep learning

Reference 1

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Observation b9a79dc8-5cdd-485e-9494-5eb5d5c8b86c · outbound

This paper cites Imagenet classification with deep convolutional neural net- works.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Imagenet classification with deep convolutional neural net- works

Reference 2

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Observation 3a045717-48ac-4fd9-8b89-a846faa2ae3e · outbound

This paper cites Visualiz- ing data using t-sne.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Visualiz- ing data using t-sne

Reference 3

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Observation 0c93c0a9-e521-475b-a4dd-bbf1b60fe33d · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Explaining and Harnessing Adversarial Examples

Reference 4

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Observation 9f77d11b-c5e7-4fd6-96f0-0609c022ad14 · outbound

This paper cites Towards deep learning models resistant to adversarial attacks.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Towards deep learning models resistant to adversarial attacks

Reference 5

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Observation 0d3bad74-abd6-433c-b967-1bc1d470ce0b · outbound

This paper cites Theoretically prin- cipled trade-off between robustness and accuracy.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Theoretically prin- cipled trade-off between robustness and accuracy

Reference 6

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Observation 60b241b8-c88d-4f21-af6c-98a4afd987d0 · outbound

This paper cites Disentan- gling adversarial robustness and generalization.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Disentan- gling adversarial robustness and generalization

Reference 7

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Observation fb284c3a-8e23-4ca5-b79d-bf153fa053e5 · outbound

This paper cites Manifold mixup: Better representations by interpolat- ing hidden states.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Manifold mixup: Better representations by interpolat- ing hidden states

Reference 8

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This paper cites Virtual adversarial training: a regularization method for supervised and semi-supervised learning.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Virtual adversarial training: a regularization method for supervised and semi-supervised learning

Reference 9

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Observation 1363ed27-9f98-4847-833f-e7cea6c406dc · outbound

This paper cites Cross-modality perturbation synergy attack for person re-identification.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Cross-modality perturbation synergy attack for person re-identification

Reference 10

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Observation 427a791f-880f-4fed-80d8-e877189b43ee · outbound

This paper cites Beyond dropout: Robust convolutional neural networks based on local feature masking.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Beyond dropout: Robust convolutional neural networks based on local feature masking

Reference 11

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Observation 3c9299f1-0e30-4f7f-b0e7-f53529a02d55 · outbound

This paper cites Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method

Reference 12

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Observation 1b5a46c7-aa8e-4009-9b90-615643537c12 · outbound

This paper cites Adversarial Learning for Neural PDE Solvers with Sparse Data.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Adversarial Learning for Neural PDE Solvers with Sparse Data

Reference 13

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Observation cacb34fe-5ecf-43ba-ad48-2c0ba51a7736 · outbound

This paper cites Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging

Reference 14

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Observation 720bf38c-f921-4499-b19e-ef2b49529db8 · outbound

This paper cites Learning internal representations by error prop- agation, parallel distributed processing, explorations in the microstructure of cognition, ed.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Learning internal representations by error prop- agation, parallel distributed processing, explorations in the microstructure of cognition, ed

Reference 15

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Observation fe8b4729-a44d-45e0-9181-ac1480c2eae7 · outbound

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

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Learning multiple layers of features from tiny images

Reference 16

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Observation b9b514dd-64e3-4f73-9626-519a5d09dd4e · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfitting.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Dropout: a simple way to prevent neural networks from overfitting

Reference 17

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Observation 4279472e-b98f-4068-8fc1-92c3764c1a5d · outbound

This paper cites Adversarial training for free! In Advances in Neu- ral Information Processing Systems (NeurIPS) , volume 32,.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Adversarial training for free! In Advances in Neu- ral Information Processing Systems (NeurIPS) , volume 32,

Reference 18

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This paper cites Person re-identification method based on grayscale feature enhance- ment.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Person re-identification method based on grayscale feature enhance- ment

Reference 19

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Observation c3d5281b-4ce8-45c5-aed2-66a750107ec5 · outbound

This paper cites Cross-Modality Attack Boosted by Gradient-Evolutionary Multiform Optimization.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Cross-Modality Attack Boosted by Gradient-Evolutionary Multiform Optimization

Reference 20

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Observation 9eb0a4e2-b9f3-48a1-8266-c9a2e23095b9 · outbound

This paper cites Exploring Color Invariance through Image-Level Ensemble Learning.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Exploring Color Invariance through Image-Level Ensemble Learning

Reference 21

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Observation 2d31c457-2ee4-49b3-9fc1-9fb9eb1cce24 · outbound

This paper cites Beyond augmentation: Empowering model robustness under extreme capture environments.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Beyond augmentation: Empowering model robustness under extreme capture environments

Reference 22

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Observation 0141ae8c-2e29-42c2-9eb6-60ad99f948a1 · outbound

This paper cites Cross-task attack: A self-supervision generative framework based on attention shift.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Cross-task attack: A self-supervision generative framework based on attention shift

Reference 23

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This paper cites A theory of learning from different domains.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation A theory of learning from different domains

Reference 24

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This paper cites Managing ex- treme ai risks amid rapid progress.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Managing ex- treme ai risks amid rapid progress

Reference 25

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

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Moment matching for multi-source domain adaptation

Reference 26

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This paper cites VisDA: The Visual Domain Adaptation Challenge.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation VisDA: The Visual Domain Adaptation Challenge

Reference 27

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This paper cites Deep hashing network for unsupervised domain adaptation.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Deep hashing network for unsupervised domain adaptation

Reference 28

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This paper cites Domain-adversarial train- ing of neural networks.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Domain-adversarial train- ing of neural networks

Reference 29

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Observation a89e7dc5-0f34-44c6-8c7b-e8022b40b7f3 · outbound

This paper cites Maximum classifier discrepancy for unsu- pervised domain adaptation.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Maximum classifier discrepancy for unsu- pervised domain adaptation

Reference 30

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Observation da4e9586-21d0-42b3-a4e4-b107ab64d8e9 · outbound

This paper cites Bridging theory and algorithm for domain adap- tation.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Bridging theory and algorithm for domain adap- tation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:13.034909Z

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Observation 0b99a43c-a55b-4c66-969e-8972c4a7e0df · outbound

This paper cites Mutual mean-teaching: Pseudo la- bel refinery for unsupervised domain adaptation on person re-identification.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Mutual mean-teaching: Pseudo la- bel refinery for unsupervised domain adaptation on person re-identification

Reference 32

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Observation 7fd1e525-0c75-442a-b376-ba8ba68c52e4 · outbound

This paper cites Tent: Fully test-time adaptation by entropy minimization.

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation Tent: Fully test-time adaptation by entropy minimization

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:12.787017Z

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Pith citing papers

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