Paper Citation Record · LEDGER

Harmonizing and Merging Source Models for CLIP-based Domain Generalization

As of 19 August 2026, this Paper Citation Record lists 77 of 77 outbound references and 2 inbound Pith citation observations for arXiv:2506.09446.

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

pith.paper-citation-record.v1
2506.09446 v1

Coverage vector

measured 77 of 77 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:56:15.926897Z

measured 79 of 79 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T19:15:52.251953Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-30T07:24:22.494557Z

Reference resolution

77 of 77 outbound references displayed

verified exact0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 87e543a3-3602-4972-a4f7-452b5a0f0186 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Learning transferable visual models from natural language supervision

Reference 1

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Observation b2ceefc6-8ea3-47fc-97ed-cedd1ceb0d11 · outbound

This paper cites Clipceil: Domain generaliza- tion through clip via channel refinement and image-text alignment.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Clipceil: Domain generaliza- tion through clip via channel refinement and image-text alignment

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:20.402527Z

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Observation 7274eb6c-d385-4e82-8072-379e45ebb159 · outbound

This paper cites Alignclip: navigating the misalignments for robust vision-language generalization.Ma- chine Learning, 114(3):1–19, 2025.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Alignclip: navigating the misalignments for robust vision-language generalization.Ma- chine Learning, 114(3):1–19, 2025

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:20.267043Z

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Observation ae8eaab9-eab8-4966-a4b5-1decc1ef4ec1 · outbound

This paper cites Domain generalization via invariant feature representation.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Domain generalization via invariant feature representation

Reference 4

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Observation d8229ad2-d0dc-41f6-98b3-2687d168eaf9 · outbound

This paper cites Learn to preserve and diversify: Parameter-efficient group with orthog- onal regularization for domain generalization.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Learn to preserve and diversify: Parameter-efficient group with orthog- onal regularization for domain generalization

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:20.005057Z

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Observation b49071f0-387a-4158-a707-ba9ba70341be · outbound

This paper cites Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes

Reference 6

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Observation 4abe1501-c168-42f9-997f-a7539bf2564d · outbound

This paper cites Leveraging vision-language models for improving domain generalization in image classification.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Leveraging vision-language models for improving domain generalization in image classification

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:19.887799Z

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Observation 9e7d4cf8-7db2-4915-a26d-44ddd49abee5 · outbound

This paper cites Soft prompt generation for domain generalization.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Soft prompt generation for domain generalization

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:19.748001Z

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Observation cc52a925-6ad5-4b73-9dd2-24550c11392b · outbound

This paper cites Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities

Reference 9

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Observation c4746a38-e2b1-4551-b492-77aabfd60526 · outbound

This paper cites Towards Efficient Pareto Set Approximation via Mixture of Experts Based Model Fusion.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Towards Efficient Pareto Set Approximation via Mixture of Experts Based Model Fusion

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T04:56:15.672978Z

Source-reported events for the cited work

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Observation 34a18371-db30-477f-b7e7-7581b1e60549 · outbound

This paper cites Averaging Weights Leads to Wider Optima and Better Generalization.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Averaging Weights Leads to Wider Optima and Better Generalization

Reference 11

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Observation 410ebca9-9920-4e77-9c8d-4ddc2e056f90 · outbound

This paper cites Swad: Domain generalization by seeking flat minima.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Swad: Domain generalization by seeking flat minima

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:19.614245Z

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Observation 118908b1-3736-4928-a681-cedc36e53067 · outbound

This paper cites Model soups: averaging weights of multiple fine-tuned models im- proves accuracy without increasing inference time.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Model soups: averaging weights of multiple fine-tuned models im- proves accuracy without increasing inference time

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:19.475876Z

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Observation 543840ad-1299-4d9b-9578-e2f094af575b · outbound

This paper cites Ensemble learning.The handbook of brain theory and neural networks, 2(1):110–125, 2002.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Ensemble learning.The handbook of brain theory and neural networks, 2(1):110–125, 2002

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:19.349268Z

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Observation 98a3ca9e-9837-4d88-a502-4d3d570874db · outbound

This paper cites Editing Models with Task Arithmetic.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Editing Models with Task Arithmetic

Reference 15

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Observation 6266e4e1-620d-457c-9a95-d2ac64f7651c · outbound

This paper cites Ties-merging: Resolving interference when merg- ing models.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Ties-merging: Resolving interference when merg- ing models

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:19.243159Z

Source-reported events for the cited work

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Observation 96e99b02-8998-4a6f-bbf5-f1c47b3b5338 · outbound

This paper cites Domain generalization: A survey.IEEE transactions on pat- tern analysis and machine intelligence, 45(4):4396–4415, 2022.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Domain generalization: A survey.IEEE transactions on pat- tern analysis and machine intelligence, 45(4):4396–4415, 2022

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:19.111385Z

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Observation e19bf243-4039-4c3b-bbbc-4f3beb250036 · outbound

This paper cites Domain generalization with small data.International Journal of Computer Vision, 132(8):3172–3190, 2024.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Domain generalization with small data.International Journal of Computer Vision, 132(8):3172–3190, 2024

Reference 18

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Observation 6b9d3332-fcf8-42f8-8f17-5daac0584d91 · outbound

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Harmonizing and Merging Source Models for CLIP-based Domain Generalization Unresolved cited work

Reference 19

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Observation ccbc2da3-9040-49fa-8fcd-71a2101e50e3 · outbound

This paper cites Csdg-fas: Closed-space domain generalization for face anti-spoofing.International Journal of Computer Vision, 132(11):4866–4879, 2024.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Csdg-fas: Closed-space domain generalization for face anti-spoofing.International Journal of Computer Vision, 132(11):4866–4879, 2024

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:18.763736Z

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Observation e0491954-6dff-45d4-879c-037f5741bb4e · outbound

This paper cites Do- main generalization with adversarial feature learning.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Do- main generalization with adversarial feature learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:18.597486Z

Source-reported events for the cited work

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Observation 9a46cf28-162a-496a-bf60-63e1399096c9 · outbound

This paper cites Deep domain generalization via conditional invariant adversarial networks.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Deep domain generalization via conditional invariant adversarial networks

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:18.432901Z

Source-reported events for the cited work

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Observation a1db834c-f5d4-435a-bdf5-b41261d8c7bf · outbound

This paper cites Multi- adversarial discriminative deep domain generalization for face presentation attack detection.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Multi- adversarial discriminative deep domain generalization for face presentation attack detection

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:18.276266Z

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Observation 462be7ac-26a5-4cca-98b9-1ddab0491914 · outbound

This paper cites Unified deep supervised domain adaptation and generalization.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Unified deep supervised domain adaptation and generalization

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:18.113129Z

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Observation d916f999-aaf5-4217-9dd1-a9e6166d5422 · outbound

This paper cites Respecting do- main relations: Hypothesis invariance for domain generalization.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Respecting do- main relations: Hypothesis invariance for domain generalization

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:17.906625Z

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Observation 3befa297-9f99-4e6e-a33e-9841c613f200 · outbound

This paper cites Addressing model vulner- ability to distributional shifts over image transformation sets.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Addressing model vulner- ability to distributional shifts over image transformation sets

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:17.741862Z

Source-reported events for the cited work

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Observation 1402d8b9-6678-4e23-a742-eb797b63db1a · outbound

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Harmonizing and Merging Source Models for CLIP-based Domain Generalization Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:56:17.547398Z

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Observation 354218bd-1fef-495b-9896-bdee0c8bb106 · outbound

This paper cites Domain randomiza- tion and pyramid consistency: Simulation-to-real generalization without accessing target domain data.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Domain randomiza- tion and pyramid consistency: Simulation-to-real generalization without accessing target domain data

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:17.402000Z

Source-reported events for the cited work

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Observation 471c79d8-e191-4ca0-a296-8d24a38a69cc · outbound

This paper cites Semi- supervised domain generalization with stochastic stylematch.In- ternational Journal of Computer Vision, 131(9):2377–2387, 2023.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Semi- supervised domain generalization with stochastic stylematch.In- ternational Journal of Computer Vision, 131(9):2377–2387, 2023

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:17.228745Z

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Observation ee3225d2-3ce5-4020-92c7-6215395e977f · outbound

This paper cites Style neophile: Constantly seeking novel styles for domain generaliza- tion.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Style neophile: Constantly seeking novel styles for domain generaliza- tion

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:17.037439Z

Source-reported events for the cited work

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Observation 118061e6-9d1c-4d5f-9acb-9dc4900f4229 · outbound

This paper cites Learning to generalize: Meta-learning for domain generalization.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Learning to generalize: Meta-learning for domain generalization

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.872926Z

Source-reported events for the cited work

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Observation 5696578f-7826-432f-b911-4fab6599b2f4 · outbound

This paper cites Shape-aware meta- learning for generalizing prostate mri segmentation to unseen do- mains.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Shape-aware meta- learning for generalizing prostate mri segmentation to unseen do- mains

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.689383Z

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Observation ee78280b-2f1f-4227-a740-eed1f722c9a4 · outbound

This paper cites Episodic training for domain generaliza- tion.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Episodic training for domain generaliza- tion

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.543573Z

Source-reported events for the cited work

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Observation 97bcb0ef-4557-48a0-b0a3-279a498e13cf · outbound

This paper cites Learning to generalize unseen domains via memory-based multi-source meta-learning for person re-identification.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Learning to generalize unseen domains via memory-based multi-source meta-learning for person re-identification

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.525207Z

Source-reported events for the cited work

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Observation 562f1af6-f97f-45e3-8231-02c3dc1b64e1 · outbound

This paper cites Domain adaptive ensemble learning.IEEE Transactions on Image Pro- cessing, 30:8008–8018, 2021.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Domain adaptive ensemble learning.IEEE Transactions on Image Pro- cessing, 30:8008–8018, 2021

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.510308Z

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Observation 7448bf45-bee4-4781-978b-b7106a2a3a11 · outbound

This paper cites Deep domain generalization with structured low-rank constraint.IEEE Transactions on Image Pro- cessing, 27(1):304–313, 2017.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Deep domain generalization with structured low-rank constraint.IEEE Transactions on Image Pro- cessing, 27(1):304–313, 2017

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.495715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 31682343-78fc-46b1-9099-496793fc27de · outbound

This paper cites Dofe: Domain-oriented feature embedding for generalizable fundus image segmentation on unseen datasets.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Dofe: Domain-oriented feature embedding for generalizable fundus image segmentation on unseen datasets

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.483944Z

Source-reported events for the cited work

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Observation c85bc9ad-08c7-456e-974a-8d3da9788696 · outbound

This paper cites Self-supervised learning across domains.IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, 44(9):5516–5528, 2021.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Self-supervised learning across domains.IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, 44(9):5516–5528, 2021

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.465501Z

Source-reported events for the cited work

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Observation 6a9e0151-2749-4d47-a286-0cf9d42c279a · outbound

This paper cites Efficient domain generalization via common-specific low-rank decomposi- tion.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Efficient domain generalization via common-specific low-rank decomposi- tion

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.447320Z

Source-reported events for the cited work

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Observation 6df22cff-c0e3-4b4f-8663-b15f72b5f72c · outbound

This paper cites Improving sample efficiency in model- free reinforcement learning from images.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Improving sample efficiency in model- free reinforcement learning from images

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.435011Z

Source-reported events for the cited work

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Observation b5d6fc78-37e1-4c06-963b-11de717b5110 · outbound

This paper cites Lora: Low-rank adaptation of large language models.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Lora: Low-rank adaptation of large language models

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.424578Z

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Observation a7110f3d-d8fa-414e-8d02-0b88c06cd260 · outbound

This paper cites Evolutionary optimization of model merging recipes.Nature Ma- chine Intelligence, pages 1–10, 2025.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Evolutionary optimization of model merging recipes.Nature Ma- chine Intelligence, pages 1–10, 2025

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.413746Z

Source-reported events for the cited work

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Observation 7995a156-2e3a-4c6e-94dc-b93968af9f64 · outbound

This paper cites Jointly Training Large Autoregressive Multimodal Models.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Jointly Training Large Autoregressive Multimodal Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T04:56:15.798393Z

Source-reported events for the cited work

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Observation 4c644580-a10e-4f37-ba45-978c43deead0 · outbound

This paper cites Diffusion soup: Model merging for text-to-image diffusion models.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Diffusion soup: Model merging for text-to-image diffusion models

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.400039Z

Source-reported events for the cited work

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Observation 04d2f96f-63d3-4785-a7a4-ec7d687f2740 · outbound

This paper cites AdaMerging: Adaptive Model Merging for Multi-Task Learning.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization AdaMerging: Adaptive Model Merging for Multi-Task Learning

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T04:56:15.805646Z

Source-reported events for the cited work

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Observation 0353a03b-798a-417a-b68d-4676b42e7184 · outbound

This paper cites Robust fine-tuning of zero-shot models.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Robust fine-tuning of zero-shot models

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.386560Z

Source-reported events for the cited work

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Observation 22906609-a883-46b1-85e9-98f268d9719f · outbound

This paper cites Model ratatouille: Recycling diverse models for out-of-distribution generalization.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Model ratatouille: Recycling diverse models for out-of-distribution generalization

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.375392Z

Source-reported events for the cited work

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Observation 625cdad2-f22c-4236-bb85-568a49dac47a · outbound

This paper cites Recognition in terra incognita.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Recognition in terra incognita

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.364222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 43b1f9a0-6faf-4092-a8a2-6ada5a114c9e · outbound

This paper cites AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T04:56:15.820501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b781e8d8-d900-4b33-be42-b9c8c19d1fdc · outbound

This paper cites Clipood: Generalizing clip to out-of- distributions.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Clipood: Generalizing clip to out-of- distributions

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.354039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e07b187e-f952-4842-a032-4db0000582eb · outbound

This paper cites Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks.Journal of Machine Learning Research, 22(241):1–124, 2021.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks.Journal of Machine Learning Research, 22(241):1–124, 2021

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T04:56:15.828266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88083e34-1654-4a0b-bd29-cdb893bf318a · outbound

This paper cites Evaluating pruning methods.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Evaluating pruning methods

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.337319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ba63dc2a-77c9-4584-bd3b-70ca89615179 · outbound

This paper cites Deeper, broader and artier domain generalization.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Deeper, broader and artier domain generalization

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.326652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 152f1146-b19c-442d-b09f-5f7d9476852f · outbound

This paper cites Unbiased look at dataset bias.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Unbiased look at dataset bias

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.313518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cd35fcfe-73ec-4650-9abf-f35410a2cccb · outbound

This paper cites Deep hashing network for unsuper- vised domain adaptation.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Deep hashing network for unsuper- vised domain adaptation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.302019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e6b025ef-f978-4133-b593-786d9153c6e4 · outbound

This paper cites Moment matching for multi-source do- main adaptation.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Moment matching for multi-source do- main adaptation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.290822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5776072a-8ee2-46ad-ac2f-ecda0291fff8 · outbound

This paper cites In Search of Lost Domain Generalization.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization In Search of Lost Domain Generalization

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T04:56:15.852449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2804aa9a-f2d0-43f0-8ba2-7e332ae4ed40 · outbound

This paper cites Decoupled weight decay regu- larization.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Decoupled weight decay regu- larization

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.280004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a8ed0e80-359f-4bc2-ac8a-07640317d72f · outbound

This paper cites Invariant Risk Minimization.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Invariant Risk Minimization

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T04:56:15.859864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22d501f9-8d39-4fa1-9f57-99e4a844c0dd · outbound

This paper cites Invariant information bottleneck for domain generalization.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Invariant information bottleneck for domain generalization

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.269577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f395b5b2-bdd2-4550-9b95-ce6fc545a6b1 · outbound

This paper cites Domain generalization by mutual-information regularization with pre-trained models.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Domain generalization by mutual-information regularization with pre-trained models

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.259137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f0ec7547-114a-48e0-8832-ad784ca801a5 · outbound

This paper cites Context-aware robust fine-tuning.International Journal of Computer Vision, 132(5):1685–1700, 2024.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Context-aware robust fine-tuning.International Journal of Computer Vision, 132(5):1685–1700, 2024

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T04:56:15.871254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27a5370c-52fe-421d-bc4f-8de34a5f360e · outbound

This paper cites An image is worth 16x16 words: Transformers for image recogni- tion at scale.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization An image is worth 16x16 words: Transformers for image recogni- tion at scale

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.240831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 677de77d-02fa-439f-bbee-c495d41e9ff1 · outbound

This paper cites Deep residual learning for image recognition.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Deep residual learning for image recognition

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.227764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1b4a6e1f-a91a-48d7-a5ad-b3cd60d0d483 · outbound

This paper cites Visualizing data using t-sne.Journal of machine learning research, 9(11), 2008.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Visualizing data using t-sne.Journal of machine learning research, 9(11), 2008

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T04:56:15.881569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4df74fc9-ab2f-4255-b0ec-ff1f7d34e299 · outbound

This paper cites mixup: Beyond empirical risk minimization.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization mixup: Beyond empirical risk minimization

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.207751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 65c13317-44fc-4dbd-bf11-82e1145476d4 · outbound

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

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Deep coral: Correlation alignment for deep domain adaptation

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.197468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9e5fbf0a-5ce4-4430-a876-aae36ed32aba · outbound

This paper cites Reducing domain gap by reducing style bias.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Reducing domain gap by reducing style bias

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.187329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ea5d2a4e-0eba-4c68-89b1-cdd61b54e74e · outbound

This paper cites Gradient matching for domain generalization.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Gradient matching for domain generalization

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.176882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 998b3951-b3fe-4e87-9f97-bb35725ae3b4 · outbound

This paper cites Selfreg: Self-supervised contrastive regularization for domain generalization.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Selfreg: Self-supervised contrastive regularization for domain generalization

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.165897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fb4b90c2-10de-463a-9280-b88f6097316c · outbound

This paper cites Exploit- ing domain-specific features to enhance domain generalization.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Exploit- ing domain-specific features to enhance domain generalization

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.154619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 91481e1b-bc9d-4322-934d-d5a45494ee55 · outbound

This paper cites Mixstyle neural networks for domain generalization and adaptation.Inter- national Journal of Computer Vision, 132(3):822–836, 2024.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Mixstyle neural networks for domain generalization and adaptation.Inter- national Journal of Computer Vision, 132(3):822–836, 2024

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.142485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8a0b4106-cf3b-4e09-a7fc-719fa2c5ae73 · outbound

This paper cites Under- standing hessian alignment for domain generalization.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Under- standing hessian alignment for domain generalization

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.129768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 480e3526-ef73-4587-8a1e-f8be963a8bd0 · outbound

This paper cites Balanced direction from multifarious choices: Arithmetic meta-learning for domain generalization.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Balanced direction from multifarious choices: Arithmetic meta-learning for domain generalization

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.118565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0d9bdc57-f1a9-4e26-a4b6-00d79373cba6 · outbound

This paper cites LFME: A simple framework for learning from mul- tiple experts in domain generalization.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization LFME: A simple framework for learning from mul- tiple experts in domain generalization

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.106661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8a712928-589e-4ac7-a402-035354a31c0b · outbound

This paper cites Learning intrin- sic invariance within intra-class for domain generalization.IEEE Transactions on Multimedia, pages 1–14, 2025.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Learning intrin- sic invariance within intra-class for domain generalization.IEEE Transactions on Multimedia, pages 1–14, 2025

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.095041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a3a319ce-4fb7-4c7e-ac1d-02e0dd156936 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Imagenet: A large-scale hierarchical image database

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:56:16.082289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation f6d345af-7335-4899-a85a-72d1b24c1874 · inbound

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging cites this paper.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Harmonizing and Merging Source Models for CLIP-based Domain Generalization

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:52.251953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae97e893-d629-47b5-9465-9045e136f90f · inbound

On the Vulnerability of Parameter-Level Defenses to Model Merging cites this paper.

On the Vulnerability of Parameter-Level Defenses to Model Merging Harmonizing and Merging Source Models for CLIP-based Domain Generalization

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-06-30T07:24:22.496063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.