Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations | Paper Citation Record

Observation e68ed32a-51f1-4adb-bdd6-063ad4c550bc · outbound

This paper cites Deep coral: Correlation alignment for deep domain adaptation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Deep coral: Correlation alignment for deep domain adaptation,

Reference 1

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Observation 0be50247-35f5-4a8a-9255-f4f8a789e7bf · outbound

This paper cites Unsupervised domain adaptation by backpropagation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Unsupervised domain adaptation by backpropagation,

Reference 2

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Observation 8168cd52-f2c0-4805-b0f8-e77f56fa74b8 · outbound

This paper cites Deep subdomain adaptation network for image classification,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Deep subdomain adaptation network for image classification,

Reference 3

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Observation 45de0d35-8e45-49da-ae18-cf0d189592e4 · outbound

This paper cites Information -theoretic regularization for multi-source domain adaptation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Information -theoretic regularization for multi-source domain adaptation,

Reference 4

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Observation 0960b708-4ee1-4396-9f34-8c50155e8af1 · outbound

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

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Moment matching for multi- source domain adaptation,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:19.260634Z

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Observation ee2110cf-0958-4287-8457-50546e24897d · outbound

This paper cites Multi -source distilling domain adaptation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Multi -source distilling domain adaptation,

Reference 6

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Observation 3ce72ba2-aea1-4991-b612-2682ad7d4914 · outbound

This paper cites Multi -source domain adaptation with mixture of joint distributions,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Multi -source domain adaptation with mixture of joint distributions,

Reference 7

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Observation ea5c623b-8aa6-4463-b5d8-846ead16cd16 · outbound

This paper cites Connecting the dots with landmarks: Discriminatively learning domain -invariant features for unsupervised domain adaptation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Connecting the dots with landmarks: Discriminatively learning domain -invariant features for unsupervised domain adaptation,

Reference 8

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Observation 865e602a-2b04-404c-a4c6-688498524aad · outbound

This paper cites Dynamic instance domain adaptation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Dynamic instance domain adaptation,

Reference 9

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Observation fb944710-ce36-4eab-afef-e19bc71a47d2 · outbound

This paper cites Prototype- based multisource domain adaptation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Prototype- based multisource domain adaptation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:19.173740Z

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Observation c90e79bb-db85-480a-97ba-c5eed3c61a43 · outbound

This paper cites Deep cocktail network: Multi-source unsupervised domain adaptation with category shift,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Deep cocktail network: Multi-source unsupervised domain adaptation with category shift,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T22:40:17.182548Z

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Observation f381a7d4-d5a1-47ad-9b6c-72c3b66914ea · outbound

This paper cites Correlation alignment for unsupervised domain adaptation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Correlation alignment for unsupervised domain adaptation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:19.143572Z

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Observation 35a0b5b9-742c-48ba-83a3-f9ac46610686 · outbound

This paper cites Learning transferable features with deep adaptation networks,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Learning transferable features with deep adaptation networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:19.126078Z

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Observation e8a1a162-12e8-453d-aea4-540bc040d9a4 · outbound

This paper cites Deep transfer learning with joint adaptation networks,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Deep transfer learning with joint adaptation networks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:19.109410Z

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Observation 473924f5-29ad-4617-a74e-95d44c09d648 · outbound

This paper cites Mutual learning of joint and separate domain alignments for multi -source domain adaptation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Mutual learning of joint and separate domain alignments for multi -source domain adaptation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:19.091329Z

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Observation 9e9148c9-f5ae-42ff-9ee9-61d2403dae06 · outbound

This paper cites A simple baseline for semi-supervised semantic segmentation with strong data augmentation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations A simple baseline for semi-supervised semantic segmentation with strong data augmentation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:19.071998Z

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Observation 2e7edc48-1a06-4f81-93fb-f56733fff607 · outbound

This paper cites Multi- representation adaptation network for cross-domain image classification,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Multi- representation adaptation network for cross-domain image classification,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:19.053276Z

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Observation 9298edbe-2510-476a-aad8-4c333e1562ef · outbound

This paper cites Aligning domain -specific distribution and classifier for cross -domain classification from multiple sources,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Aligning domain -specific distribution and classifier for cross -domain classification from multiple sources,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:19.034726Z

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Observation b89a4df6-7db7-4fe8-8b49-a5c6dd7ad6c1 · outbound

This paper cites Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:19.015943Z

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Observation dc1c57fc-80a3-4e14-ba85-7f9e5a8b235c · outbound

This paper cites Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.993640Z

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Observation f895f2b7-05ec-4555-90b6-af27c35b7b51 · outbound

This paper cites Adversarial entropy optimization for unsupervised domain adaptation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Adversarial entropy optimization for unsupervised domain adaptation,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.972118Z

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Observation 48e10a81-b797-48bc-abe7-a80c4ad8d18d · outbound

This paper cites Active adversarial domain adaptation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Active adversarial domain adaptation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.953192Z

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Observation f71dbb3b-e79c-4b36-aacf-901c32f71b50 · outbound

This paper cites Domain adaptation via incremental confidence samples into classification,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Domain adaptation via incremental confidence samples into classification,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.933774Z

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Observation 72140334-f93e-42d3-b7de-2edc20fa2c33 · outbound

This paper cites Pseudo -loss confidence metric for semi -supervised few -shot learning,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Pseudo -loss confidence metric for semi -supervised few -shot learning,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.913924Z

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Observation 6dffbf10-9770-4bea-973c-cc8040cdeec6 · outbound

This paper cites Adversarial discriminative domain adaptation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Adversarial discriminative domain adaptation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.887879Z

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Observation 65cf264c-51c9-4f30-805f-b154c00cfaf7 · outbound

This paper cites Coupled generative adversarial networks,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Coupled generative adversarial networks,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.862392Z

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Observation f766ae22-425e-40c0-8f9f-4bb04a9eaa70 · outbound

This paper cites Maximum classifier discrepancy for unsupervised domain adaptation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Maximum classifier discrepancy for unsupervised domain adaptation,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.841123Z

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Observation 6425ef13-a12b-4cec-90a1-d9123970c72f · outbound

This paper cites Multi -source contribution learning for domain adaptation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Multi -source contribution learning for domain adaptation,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.823448Z

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Observation e60e43e6-3841-4eb9-8a80-52335ea3db40 · outbound

This paper cites Stem: An approach to multi -source domain adaptation with guarantees,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Stem: An approach to multi -source domain adaptation with guarantees,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.803826Z

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Observation 2bfa0188-351b-4daf-8226-eb6621d14edd · outbound

This paper cites Hard -aware deeply cascaded embedding,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Hard -aware deeply cascaded embedding,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.774494Z

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Observation 6490f3a8-9323-4d17-aeff-762c4d4f772b · outbound

This paper cites Model adaptation with synthetic and real data for semantic dense foggy scene understanding,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Model adaptation with synthetic and real data for semantic dense foggy scene understanding,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T22:40:17.332123Z

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Observation 56854655-6ffd-407b-ad71-608125bbc323 · outbound

This paper cites Unsupervised intra-domain adaptation for semantic segmentation through self - supervision,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Unsupervised intra-domain adaptation for semantic segmentation through self - supervision,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.732782Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bf817833-1731-4440-bfad-a80b2dccc141 · outbound

This paper cites Instance credibility inference for few - shot learning,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Instance credibility inference for few - shot learning,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.711693Z

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Observation 27e4e8e8-f2e8-4b85-8335-45f59a7022f4 · outbound

This paper cites Instance -specific and model-adaptive supervision for semi -supervised semantic segmentation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Instance -specific and model-adaptive supervision for semi -supervised semantic segmentation,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.686838Z

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Observation 7c44f9d8-9cc7-4e15-b06e-d79e3a8727c3 · outbound

This paper cites Towards fewer annotations: Active learning via region impurity and prediction uncertainty for domain adaptive semantic segmentation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Towards fewer annotations: Active learning via region impurity and prediction uncertainty for domain adaptive semantic segmentation,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.662636Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e97503c2-928b-4d75-a46e-134aa4dec1d7 · outbound

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

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Mixstyle neural networks for domain generalization and adaptation,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.640226Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 22a2b22b-b834-4bf1-ab8e-75aaa1dc5a8a · outbound

This paper cites mixup: Beyond empirical risk minimization,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations mixup: Beyond empirical risk minimization,

Reference 37

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Observation 69af0a8a-4485-4e49-b166-0d83d5f5a66e · outbound

This paper cites Cutmix: Regularization strategy to train strong classifiers with localizable features,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Cutmix: Regularization strategy to train strong classifiers with localizable features,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.594165Z

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Observation 18eb0b0c-dcab-489d-8398-95eccbf53d19 · outbound

This paper cites Autoaugment: Learning augmentation strategies from data,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Autoaugment: Learning augmentation strategies from data,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.573962Z

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Observation bfb31b35-ea3f-4652-b24c-a823b6138509 · outbound

This paper cites Randaugment: Practical automated data augmentation with a reduced search space,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Randaugment: Practical automated data augmentation with a reduced search space,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.547584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 85027991-cadc-4f76-813f-d2ea68df29ab · outbound

This paper cites Multi -source unsupervised domain adaptation via pseudo target domain,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Multi -source unsupervised domain adaptation via pseudo target domain,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.520766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 11014a8e-cec4-4e19-980d-a382d8ff1f08 · outbound

This paper cites Learning to combine: Knowledge aggregation for multi -source domain adaptation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Learning to combine: Knowledge aggregation for multi -source domain adaptation,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.497954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8258e346-a2bc-49b7-9cf2-ebe376f7e61f · outbound

This paper cites A new progressive multisource domain adaptation network with weighted decision fusion,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations A new progressive multisource domain adaptation network with weighted decision fusion,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.471938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bf0c4329-fc6c-428c-9add-08ca37347416 · outbound

This paper cites T-svdnet: Exploring high-order prototypical correlations for multi -source domain adaptation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations T-svdnet: Exploring high-order prototypical correlations for multi -source domain adaptation,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.452448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 587715ac-0db5-497a-bf4d-b9daa15a88c3 · outbound

This paper cites Self -paced supervision for multi- source domain adaptation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Self -paced supervision for multi- source domain adaptation,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.434099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fced1a7c-d9b6-4a96-a568-088700e5c6d9 · outbound

This paper cites Domain -specific feature elimination: multi- source domain adaptation for image classification,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Domain -specific feature elimination: multi- source domain adaptation for image classification,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.415551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 291e98ea-4964-4005-a7ab-6c8ed6ff26b0 · outbound

This paper cites Adapting visual category models to new domains,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Adapting visual category models to new domains,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.391882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 819ec1f4-6b72-41d4-a48c-4a784ca99b6c · outbound

This paper cites Deep hashing network for unsupervised domain adaptation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Deep hashing network for unsupervised domain adaptation,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.368007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e9728927-896b-4f92-85c0-5d4c49339a3b · outbound

This paper cites Automix: Unveiling the power of mixup for stronger classifiers,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Automix: Unveiling the power of mixup for stronger classifiers,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.350755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1f99e977-6294-41a9-90c7-f9f2a9f69604 · outbound

This paper cites Training multi -source domain adaptation network by mutual information estimation and minimization,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Training multi -source domain adaptation network by mutual information estimation and minimization,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.332949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2cd32f55-ce56-4159-ba26-493d316fe81b · outbound

This paper cites Semi-sup.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Semi-sup

Reference 51

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T22:40:19.349307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation eb067307-f995-4ee8-acdc-5e8000e54d40 · outbound

This paper cites Deeper, broader and artier domain generalization,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Deeper, broader and artier domain generalization,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.314811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a7b8b4e2-ae41-4ed6-a7f8-7ecf1d1bfccf · outbound

This paper cites Geodesic flow kernel for unsupervised domain adaptation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Geodesic flow kernel for unsupervised domain adaptation,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.297151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 74b89907-63fe-47d8-af18-11941e1fc066 · outbound

This paper cites Deep residual learning for image recognition,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Deep residual learning for image recognition,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-10T22:40:17.507411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01c2f065-a820-4cdf-b0aa-f81fca8aaea0 · outbound

This paper cites Weighted correlation embedding learning for domain adaptation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Weighted correlation embedding learning for domain adaptation,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.267404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3f1b1ec8-b4fb-4d4a-b866-131cb929f4a1 · outbound

This paper cites Low-Rank Correlation Learning for Unsupervised Domain Adaptation ,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Low-Rank Correlation Learning for Unsupervised Domain Adaptation ,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.250714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e534ef03-5f95-49c0-b7e7-bd893ab7eac3 · outbound

This paper cites Guided discrimination and correlation subspace learning for domain adaptation ,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Guided discrimination and correlation subspace learning for domain adaptation ,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.233539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ec0d6126-7ba0-4d77-b837-25fcd01d7ff3 · outbound

This paper cites Deep Domain Adaptation With Max -Margin Principle for Cross -Project Imbalanced Software Vulnerability Detection ,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Deep Domain Adaptation With Max -Margin Principle for Cross -Project Imbalanced Software Vulnerability Detection ,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.217091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation feb082bc-2dee-488b-ac8e-439aa2025a5a · outbound

This paper cites A Class-aware Optimal Transport Approach with Higher-Order Moment Matching for Unsupervised Domain Adaptation.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations A Class-aware Optimal Transport Approach with Higher-Order Moment Matching for Unsupervised Domain Adaptation

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-10T22:40:17.538918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c8f882e-e711-4023-9cf8-e00eaa6e745c · outbound

This paper cites Test-time Adaptation against Multi- modal Reliability Bias ,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Test-time Adaptation against Multi- modal Reliability Bias ,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.200052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 893b4c8d-248b-4187-93db-d2d9ee7c560f · outbound

This paper cites Cross-domain adaptive clustering for semi - supervised domain adaptation ,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Cross-domain adaptive clustering for semi - supervised domain adaptation ,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.181965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bbae3622-dcf4-4732-a4b8-321537e8ca12 · outbound

This paper cites A discriminatively deep fusion approach with improved conditional GAN (im -cGAN) for facial expression recognition,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations A discriminatively deep fusion approach with improved conditional GAN (im -cGAN) for facial expression recognition,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.164728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 55bacfbd-c01c-4972-8e26-412788138cca · outbound

This paper cites Robust object re -identification with coupled noisy labels,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Robust object re -identification with coupled noisy labels,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.145523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e2231af0-41f2-48e8-a9ee-6e28fb119d8a · outbound

This paper cites Graph matching with bi -level noisy correspondence,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Graph matching with bi -level noisy correspondence,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.125262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a9158726-d8f8-4eff-b8dd-fbda9d337192 · outbound

This paper cites Robust multi -view clustering with incomplete information ,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Robust multi -view clustering with incomplete information ,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.106749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 30133417-8e99-400d-832b-bbab75330ec9 · outbound

This paper cites Rethinking the inception architecture for computer vision,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Rethinking the inception architecture for computer vision,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.085725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7e8ce3db-c667-41bb-9e2c-ad3f1742d5be · outbound

This paper cites Densely connected convolutional networks,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Densely connected convolutional networks,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.068043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4d8d294e-d7f7-4b7e-a924-d32912a3613d · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.052076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b4bcb26d-8783-4c22-a824-cc1b3269efd6 · outbound

This paper cites Euclidean distance mapping ,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Euclidean distance mapping ,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.035750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 510971b5-b1d1-40a4-9abb-1d58c99064a9 · outbound

This paper cites Wasserstein distance guided representation learning for domain adaptation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Wasserstein distance guided representation learning for domain adaptation,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.018098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d62be130-6a00-4078-a8d9-f4551059c50f · outbound

This paper cites Federated Domain Adaptation via Transformer for Multi -site Alzheimer’s Disease Diagnosis,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Federated Domain Adaptation via Transformer for Multi -site Alzheimer’s Disease Diagnosis,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:18.000326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9b902310-b6d7-44a3-ada1-f2604e35cec8 · outbound

This paper cites Graph Convolution and Self -attention Enhanced CNN with Domain Adaptation for Multi -site COVID- 19 Diagnosis,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Graph Convolution and Self -attention Enhanced CNN with Domain Adaptation for Multi -site COVID- 19 Diagnosis,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:17.982382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8261b3e0-d00f-4dcc-8569-d876ca361faa · outbound

This paper cites A new progressive multisource domain adaptation network with weighted decision fusion,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations A new progressive multisource domain adaptation network with weighted decision fusion,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:17.960920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c5ba21d6-03bf-4418-87e5-8aab5de4bca5 · outbound

This paper cites Multi -source collaborative contrastive learning for decentralized domain adaptation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Multi -source collaborative contrastive learning for decentralized domain adaptation,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:17.932665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4ffc962e-1d3b-4f1b-ba33-c7ba058c67e8 · outbound

This paper cites Multi -source transfer learning via optimal transport feature ranking for EEG classification,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Multi -source transfer learning via optimal transport feature ranking for EEG classification,

Reference 75

Resolution: metadata mismatch; raw_fallback, observed 2026-08-10T22:40:17.818414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 553119a4-a83f-4cbe-b71b-c79fb1cab34e · outbound

This paper cites DANE: A dual -level alignment network with ensemble learning for multi -source domain adaptation,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations DANE: A dual -level alignment network with ensemble learning for multi -source domain adaptation,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:17.913393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 813d5632-ee05-4382-b164-b4d2437e8238 · outbound

This paper cites Adaptive intermediate class -wise distribution alignment: A universal domain adaptation and generalization method for machine fault diagnosis,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Adaptive intermediate class -wise distribution alignment: A universal domain adaptation and generalization method for machine fault diagnosis,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:17.895270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bc009b06-e111-4957-88f6-4419c05d70f9 · outbound

This paper cites Learning with alignments: Tackling the inter - and intra-domain shifts for cross -multidomain facial expression recognition,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Learning with alignments: Tackling the inter - and intra-domain shifts for cross -multidomain facial expression recognition,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:17.874643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b6b31161-1e42-478b-a7e9-e1f9ae169335 · outbound

This paper cites Domain adaptation in reinforcement learning: a comprehensive and systematic study,.

Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations Domain adaptation in reinforcement learning: a comprehensive and systematic study,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:17.855407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Reference resolution

Outbound references

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