Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation | Paper Citation Record

Observation 552bf1d9-9d91-471a-973d-58bba40346ff · outbound

This paper cites Self-training: A survey.Neurocomputing, 616:128904, 2025.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Self-training: A survey.Neurocomputing, 616:128904, 2025

Reference 1

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Observation 6596a9b0-1364-4a48-aaeb-2691863ec4f2 · outbound

This paper cites Foundation models defining a new era in vision: A survey and outlook.IEEE Transac- tions on Pattern Analysis and Machine Intelligence, 47(4):2245–2264, 2025.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Foundation models defining a new era in vision: A survey and outlook.IEEE Transac- tions on Pattern Analysis and Machine Intelligence, 47(4):2245–2264, 2025

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.834945Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 728bb467-f3b1-418e-b824-672ad280bae4 · outbound

This paper cites Mixmatch: A holistic approach to semi-supervised learning.Ad- vances in Neural Information Processing Systems, 32, 2019.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Mixmatch: A holistic approach to semi-supervised learning.Ad- vances in Neural Information Processing Systems, 32, 2019

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.817612Z

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Observation ddda0237-ea7a-47ab-9571-9fbf5c7a8cc4 · outbound

This paper cites Semi-supervised vision transformers at scale.Advances in Neural Information Processing Systems, 35:25697–25710, 2022.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Semi-supervised vision transformers at scale.Advances in Neural Information Processing Systems, 35:25697–25710, 2022

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.800341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 489102f1-ffca-49f0-9e86-b5a96560fade · outbound

This paper cites Emerging properties in self-supervised vision transform- ers.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Emerging properties in self-supervised vision transform- ers

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.782570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b3290dcc-d6b6-459a-bdcf-3e07b4e89f33 · outbound

This paper cites SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning

Reference 6

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Observation 4eed8215-3f57-496c-9ed7-39c29beebd4a · outbound

This paper cites Adaptformer: Adapting vision transformers for scalable visual recognition.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Adaptformer: Adapting vision transformers for scalable visual recognition

Reference 7

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Observation 057ed284-9275-4494-ac50-ba06601d0e01 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-14T04:20:35.537318Z

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Observation f3714f59-d40b-4dca-a5d8-2d4d4170a3b5 · outbound

This paper cites Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-14T04:20:35.543475Z

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Observation 4f23144b-4792-40f5-b201-dced8e269217 · outbound

This paper cites Co-teaching: Robust training of deep neural networks with extremely noisy labels.Advances in neural information processing systems, 31, 2018.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Co-teaching: Robust training of deep neural networks with extremely noisy labels.Advances in neural information processing systems, 31, 2018

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T04:20:35.548702Z

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Observation 9e466a55-cbe1-412e-b4f5-5abcc7b369e9 · outbound

This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 11

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Observation fff6fb8e-f8a4-4dce-b901-123cfb905f78 · outbound

This paper cites Research on the application of electronic technology of internet of things in smart city.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Research on the application of electronic technology of internet of things in smart city

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.737633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1d3e5368-ae9c-4812-beb5-bed0c6a48e85 · outbound

This paper cites Trustmatch: mitigating pseudo-label bias in semi- supervised learning with trust-aware refinement.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Trustmatch: mitigating pseudo-label bias in semi- supervised learning with trust-aware refinement

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.720121Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1e88ab17-a527-4ba7-a907-6ea8e6cac989 · outbound

This paper cites Research on pedestrian tracking algorithm based on deep learning.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Research on pedestrian tracking algorithm based on deep learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.699661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fa4c13c5-66b4-4c6d-bc05-f4ddaccd04e3 · outbound

This paper cites Research on surface defect detection method of metal workpiece based on machine learning.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Research on surface defect detection method of metal workpiece based on machine learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.680331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 58bff2b9-42f5-47f4-8a46-ac899662b77e · outbound

This paper cites 4s-classifier: Empowering conservation through semi-supervised learning for rare and endangered species.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation 4s-classifier: Empowering conservation through semi-supervised learning for rare and endangered species

Reference 16

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Source-reported events for the cited work

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Observation baa1f50a-35f5-42aa-927c-3ef53155170c · outbound

This paper cites Semi-vim: bidi- rectional state space model for mitigating label imbalance in semi-supervised learning.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Semi-vim: bidi- rectional state space model for mitigating label imbalance in semi-supervised learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.649818Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation aa0803c8-573d-4157-ab14-c9098a9f98cf · outbound

This paper cites Revisiting Chain-of-Thought Reasoning under Limited Supervision: Semi-supervised Chain-of-Thought Learning.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Revisiting Chain-of-Thought Reasoning under Limited Supervision: Semi-supervised Chain-of-Thought Learning

Reference 18

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Observation a36a7078-3b6b-4d0b-b461-515990fa0a89 · outbound

This paper cites Semi-Supervised Vision-Language-Action Model.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Semi-Supervised Vision-Language-Action Model

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-14T04:20:36.012732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c849e820-d369-4439-9c1b-e16e84fd2767 · outbound

This paper cites Trico: Triadic game-theoretic co-training for robust semi-supervised learning.Advances in Neural Information Processing Systems, 38: 87545–87570, 2026.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Trico: Triadic game-theoretic co-training for robust semi-supervised learning.Advances in Neural Information Processing Systems, 38: 87545–87570, 2026

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.631519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5ac1ddf4-cfe2-4733-8cf0-f67b9a7ac62f · outbound

This paper cites Newton-coupled dual-teacher semi-supervised learning framework.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Newton-coupled dual-teacher semi-supervised learning framework

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.611531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5bf8cf63-bb01-4d53-a87d-77027b629e2f · outbound

This paper cites Token- aware representation augmentation for fine-grained semi-supervised learning.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Token- aware representation augmentation for fine-grained semi-supervised learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.590267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cb3b6928-3ad6-461f-9e5e-a4507fe3951a · outbound

This paper cites Be- yond data augmentation: Energy-based kuramoto neurons for semi-supervised learn- ing.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Be- yond data augmentation: Energy-based kuramoto neurons for semi-supervised learn- ing

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.568769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5bc91bb2-67ea-4210-9033-a3c880b81d83 · outbound

This paper cites Parameter- efficient transfer learning for nlp.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Parameter- efficient transfer learning for nlp

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.550058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 39561324-5b1d-4d8d-9e0a-a685df411842 · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Liang Wang, Weizhu Chen, et al.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Liang Wang, Weizhu Chen, et al

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.532507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cccfeb7a-1272-49d3-b546-818ff634e368 · outbound

This paper cites Visual prompt tuning.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Visual prompt tuning

Reference 26

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Observation acb9f70f-2eed-4b5f-ab99-5b28b0064ed1 · outbound

This paper cites Nlnl: Negative learning for noisy labels.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Nlnl: Negative learning for noisy labels

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.503003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d5a56f6c-9adc-49b3-bc82-93b076e22aeb · outbound

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

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Pseudo-label: The simple and efficient semi-supervised learn- ing method for deep neural networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.483007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a950ec97-c607-449f-a3fe-0ad65f0aaebe · outbound

This paper cites DivideMix: Learning with Noisy Labels as Semi-supervised Learning.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-14T04:20:35.657650Z

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Observation ea47879a-d183-48dc-8e48-9b6a63f4cbc7 · outbound

This paper cites Unlabeled data vs.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Unlabeled data vs

Reference 30

Resolution: verified exact; raw_fallback, observed 2026-08-14T04:20:35.953428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f158c11c-3b10-42ef-ac10-1c91bee0dc4f · outbound

This paper cites Fine-tuning is fine, if calibrated.Advances in Neural Information Processing Systems, 37:136084– 136119, 2024.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Fine-tuning is fine, if calibrated.Advances in Neural Information Processing Systems, 37:136084– 136119, 2024

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.465693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9709362b-c5ee-4a91-923e-f022bcdc970d · outbound

This paper cites Lessons and insights from a unifying study of parameter- efficient fine-tuning (peft) in visual recognition.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Lessons and insights from a unifying study of parameter- efficient fine-tuning (peft) in visual recognition

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.448868Z

Source-reported events for the cited work

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Observation f1fa3a5f-2772-48cb-a1f3-39b4a3110165 · outbound

This paper cites Enhancing clip with clip: Exploring pseudolabeling for limited-label prompt tuning.Advances in Neural Infor- mation Processing Systems, 36:60984–61007, 2023.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Enhancing clip with clip: Exploring pseudolabeling for limited-label prompt tuning.Advances in Neural Infor- mation Processing Systems, 36:60984–61007, 2023

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.431583Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6e8545bb-230b-4868-a344-d99897e67351 · outbound

This paper cites Boosting semi-supervised learning by bridging high and low-confidence predictions.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Boosting semi-supervised learning by bridging high and low-confidence predictions

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.412896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation de1e4af5-22fd-4e9f-b69a-b9d4e2233af2 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation DINOv2: Learning Robust Visual Features without Supervision

Reference 35

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Source-reported events for the cited work

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Observation 124445ec-51f9-4687-a935-26d89db3e806 · outbound

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

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Learning transferable visual models from natural language supervision

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.391715Z

Source-reported events for the cited work

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Observation 8dc7da46-cc61-435b-a6ef-31605a48c3d1 · outbound

This paper cites Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.372893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1146cc55-0370-4e00-8c8a-7743cd1b6d07 · outbound

This paper cites Mean teachers are better role models: Weight- averaged consistency targets improve semi-supervised deep learning results.Advances in Neural Information Processing Systems, 30, 2017.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Mean teachers are better role models: Weight- averaged consistency targets improve semi-supervised deep learning results.Advances in Neural Information Processing Systems, 30, 2017

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.356341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5a849c00-0c3f-465f-9eac-e3f411dcf214 · outbound

This paper cites Van Engelen and Holger H.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Van Engelen and Holger H

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.338708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4e729e4e-a54f-40ad-8458-9e95b971ef6b · outbound

This paper cites an unresolved cited work.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Unresolved cited work

Reference 40

Resolution: unresolved; raw_fallback, observed 2026-08-14T04:20:36.319935Z

Source-reported events for the cited work

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Observation 79a7753f-995b-472b-9b01-7c785fbde6bd · outbound

This paper cites FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning

Reference 41

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Source-reported events for the cited work

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Observation 9b71d8d2-e976-4ecd-9d2f-1fb068f99459 · outbound

This paper cites Combating noisy labels by agree- ment: A joint training method with co-regularization.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Combating noisy labels by agree- ment: A joint training method with co-regularization

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.302556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5a4f3cd2-02be-4537-a331-11b7e66fe0c1 · outbound

This paper cites Semi-supervised vision transformers.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Semi-supervised vision transformers

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.284481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6d8cdccd-eee8-488e-b5dd-d7962156b6e4 · outbound

This paper cites Traditional machine learning models for building energy performance prediction: A comparative research.Machine Learning Research, 8(1): 1–8, 2023.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Traditional machine learning models for building energy performance prediction: A comparative research.Machine Learning Research, 8(1): 1–8, 2023

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.265606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation be4b5fda-ef88-4e37-b7af-d6f818b17f2c · outbound

This paper cites an unresolved cited work.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Unresolved cited work

Reference 45

Resolution: unresolved; raw_fallback, observed 2026-08-14T04:20:36.242016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3c9787d2-ccb7-4570-ba2e-1b43db883a3c · outbound

This paper cites V-petl bench: A unified vi- sual parameter-efficient transfer learning benchmark.Advances in Neural Information Processing Systems, 37:80522–80535, 2024.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation V-petl bench: A unified vi- sual parameter-efficient transfer learning benchmark.Advances in Neural Information Processing Systems, 37:80522–80535, 2024

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.223540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 228a5e56-118f-4d46-90f6-ac7e49c42732 · outbound

This paper cites A survey on deep semi- supervised learning.IEEE Transactions on Knowledge and Data Engineering, 35(9): 8934–8954, 2022.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation A survey on deep semi- supervised learning.IEEE Transactions on Knowledge and Data Engineering, 35(9): 8934–8954, 2022

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.201068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c49eb2f3-2068-41df-8f00-8518fb0076c2 · outbound

This paper cites Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling.Advances in Neural Information Processing Systems, 34: 18408–18419, 2021.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling.Advances in Neural Information Processing Systems, 34: 18408–18419, 2021

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.178764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cc60bc08-9cb0-4dd0-af5e-47485e7a1b2b · outbound

This paper cites Re- visiting semi-supervised learning in the era of foundation models.Advances in Neural Information Processing Systems, 38:59295–59324, 2026.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation Re- visiting semi-supervised learning in the era of foundation models.Advances in Neural Information Processing Systems, 38:59295–59324, 2026

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:20:36.157538Z

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