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Paper Citation Record · LEDGER

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment

As of 20 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2509.10134.

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

pith.paper-citation-record.v1
2509.10134 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T18:10:28.769885Z

measured 33 of 33 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

33 of 33 outbound references displayed

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  • unresolved29
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Outbound references

Observation e7dc625c-e502-4e52-8e4e-46d4b42c01cd · outbound

This paper cites Unsupervised robust domain adaptation without source data.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Unsupervised robust domain adaptation without source data

Reference 1

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Observation 3c102c77-a696-4938-8e4e-f0e7729f7ef6 · outbound

This paper cites Source-relaxed domain adaptation for image segmentation.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Source-relaxed domain adaptation for image segmentation

Reference 2

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Observation 7d00a0ce-8fe1-41a6-ab8c-f85d18a4fd87 · outbound

This paper cites Weight uncertainty in neural network.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Weight uncertainty in neural network

Reference 3

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Observation f6d8bea6-0ba3-4c92-9f60-55915ea24c50 · outbound

This paper cites Local con- trastive loss with pseudo-label based self-training for semi-supervised medical image segmentation.Medical Image Analysis, 87:102792, 2023.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Local con- trastive loss with pseudo-label based self-training for semi-supervised medical image segmentation.Medical Image Analysis, 87:102792, 2023

Reference 4

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Observation 502ab91a-3adc-4bea-ab19-5ad169cfd3bb · outbound

This paper cites Source-free do- main adaptive fundus image segmentation with denoised pseudo-labeling.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Source-free do- main adaptive fundus image segmentation with denoised pseudo-labeling

Reference 5

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Observation b8c0811a-0f8a-4963-8952-a69dd6dfdec3 · outbound

This paper cites Uc-sfda: Source-free domain adapta- tion via uncertainty prediction and evidence-based contrastive learning.Knowledge- Based Systems, 275:110728, 2023.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Uc-sfda: Source-free domain adapta- tion via uncertainty prediction and evidence-based contrastive learning.Knowledge- Based Systems, 275:110728, 2023

Reference 6

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Observation 983d0398-fd95-4108-9d02-cf48fce1a3d6 · outbound

This paper cites Encoder-decoder with atrous separable convolution for semantic image seg- mentation.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Encoder-decoder with atrous separable convolution for semantic image seg- mentation

Reference 7

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Observation b5a4d446-c7cb-4e1e-916d-f6d90c2eda5a · outbound

This paper cites Unsupervised domain adaptive fundus image segmentation with category-level regu- larization.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Unsupervised domain adaptive fundus image segmentation with category-level regu- larization

Reference 8

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Observation fa03cd30-a5b6-4b6d-aa24-25c2d5e26893 · outbound

This paper cites Rim-one: An open retinal image database for optic nerve evaluation.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Rim-one: An open retinal image database for optic nerve evaluation

Reference 9

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Observation 9fa8d6ea-725c-4ee1-990a-ade7be53ee78 · outbound

This paper cites Context-aware pseudo-label re- finement for source-free domain adaptive fundus image segmentation.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Context-aware pseudo-label re- finement for source-free domain adaptive fundus image segmentation

Reference 10

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Observation a00ecfd1-ebae-495b-9539-d45ef8be1fa0 · outbound

This paper cites Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data.Advances in Neural Information Processing Systems, 34:3635–3649, 2021.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data.Advances in Neural Information Processing Systems, 34:3635–3649, 2021

Reference 11

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Observation 81ae99f8-75c4-4864-91a2-c7dbfc68b584 · outbound

This paper cites What uncertainties do we need in bayesian deep learning for computer vision?Advances in neural information processing systems, 30, 2017.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment What uncertainties do we need in bayesian deep learning for computer vision?Advances in neural information processing systems, 30, 2017

Reference 12

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Observation 5380f259-05dd-4bea-847a-4ebea6d034b3 · outbound

This paper cites Pseudo-label: The simple and efficient semi-supervised learn- ing method for deep neural networks.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Pseudo-label: The simple and efficient semi-supervised learn- ing method for deep neural networks

Reference 13

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Observation 3c201076-e61f-41a0-bca1-307ac238e9c5 · outbound

This paper cites an unresolved cited work.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Unresolved cited work

Reference 14

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Observation a86e4193-064e-4437-856e-f1f8ce891aeb · outbound

This paper cites Maximum density divergence for domain adaptation.IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(11):3918–3930, 2021.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Maximum density divergence for domain adaptation.IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(11):3918–3930, 2021

Reference 15

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Observation 4f3a7040-8354-4dda-916a-38b9c1ab75c5 · outbound

This paper cites A comprehensive survey on source-free domain adaptation.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(8):5743–5762, 2024.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment A comprehensive survey on source-free domain adaptation.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(8):5743–5762, 2024

Reference 16

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Observation 0a13d57a-b4f3-4af7-8a4d-66aef2062fad · outbound

This paper cites Robust source-free domain adaptation for fundus image segmentation.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Robust source-free domain adaptation for fundus image segmentation

Reference 17

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Observation 04539fd7-3d1a-4557-812b-5177915f9841 · outbound

This paper cites Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation

Reference 18

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Observation 57e75ea3-dcd6-4002-af90-c2a887e56a2e · outbound

This paper cites Deep unsupervised domain adaptation: A review of recent advances and perspectives.APSIPA Transactions on Signal and Information Process- ing, 11(1), 2022.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Deep unsupervised domain adaptation: A review of recent advances and perspectives.APSIPA Transactions on Signal and Information Process- ing, 11(1), 2022

Reference 19

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Observation 6010a060-e546-4072-b9a5-23ec08c295c3 · outbound

This paper cites Open compound domain adaptation.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Open compound domain adaptation

Reference 20

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Observation 8461b2c1-8406-48b5-9210-45a91162ce9d · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Representation Learning with Contrastive Predictive Coding

Reference 21

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Observation 3cec517f-682a-46c0-a11c-611755df8b35 · outbound

This paper cites Bathula, Andrés Diaz-Pinto, Ruogu Fang, Pheng-Ann Heng, Jeyoung Kim, JoonHo Lee, Joonseok Lee, Xiaoxiao Li, Peng Liu, Shuai Lu, Balamurali Murugesan, Valery Naranjo, Sai Samarth R.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Bathula, Andrés Diaz-Pinto, Ruogu Fang, Pheng-Ann Heng, Jeyoung Kim, JoonHo Lee, Joonseok Lee, Xiaoxiao Li, Peng Liu, Shuai Lu, Balamurali Murugesan, Valery Naranjo, Sai Samarth R

Reference 22

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Observation 8d82fe5d-f2c2-48fb-b971-7d2c83e39ce4 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 24

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Observation 3b5be7a9-f3fc-48a5-a510-92bc8b9af1af · outbound

This paper cites A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis.JSM Biomedical Imaging Data Papers, 2(1):1004, 2015.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis.JSM Biomedical Imaging Data Papers, 2(1):1004, 2015

Reference 25

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Observation d2c74987-1ecd-47b2-b3e8-e343062b71a3 · outbound

This paper cites Source-free do- main adaptive fundus image segmentation with class-balanced mean teacher.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Source-free do- main adaptive fundus image segmentation with class-balanced mean teacher

Reference 26

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Observation f75a4d18-8e7d-4957-af52-6921d224fafa · outbound

This paper cites Nearest Neighborhood-Based Deep Clustering for Source Data-absent Unsupervised Domain Adaptation.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Nearest Neighborhood-Based Deep Clustering for Source Data-absent Unsupervised Domain Adaptation

Reference 27

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Observation 2e43804b-8af4-4090-978e-b7f7f2b799c9 · outbound

This paper cites Uncertainty and energy based loss guided semi-supervised semantic segmentation.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Uncertainty and energy based loss guided semi-supervised semantic segmentation

Reference 28

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Observation 8216b727-e2cf-42f0-b930-c111e46aaf54 · outbound

This paper cites Vibashan, V.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Vibashan, V

Reference 29

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Observation 4948b351-7374-424f-b6f6-d8b7b9384a67 · outbound

This paper cites Advent: Adversarial entropy minimization for domain adaptation in semantic segmen- tation.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Advent: Adversarial entropy minimization for domain adaptation in semantic segmen- tation

Reference 30

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Observation 26a04dc0-7947-4a58-8e15-fc1eb9b7adb4 · outbound

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

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Tent: Fully test-time adaptation by entropy minimization

Reference 31

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Observation 7b156c61-aee2-43ff-95b2-50353494c2d8 · outbound

This paper cites Boundary and entropy-driven adversarial learning for fundus image segmentation.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Boundary and entropy-driven adversarial learning for fundus image segmentation

Reference 32

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Observation d972c0c9-9fe9-4d59-ae16-3c689f8cec2b · outbound

This paper cites Patch- based output space adversarial learning for joint optic disc and cup segmentation.IEEE transactions on medical imaging, 38(11):2485–2495, 2019.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Patch- based output space adversarial learning for joint optic disc and cup segmentation.IEEE transactions on medical imaging, 38(11):2485–2495, 2019

Reference 33

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Observation 77339815-4000-4b71-9d1e-6494f81b3cdf · outbound

This paper cites Divide and contrast: Source-free domain adaptation via adaptive contrastive learning.

Grad-CL: Source Free Domain Adaptation with Gradient Guided Feature Disalignment Divide and contrast: Source-free domain adaptation via adaptive contrastive learning

Reference 34

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