Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T22:40:29.584995Z
Paper Citation Record · LEDGER
As of 18 August 2026, this Paper Citation Record lists 94 of 94 outbound references and 0 inbound Pith citation observations for arXiv:2501.01126.
A citation records a reference. It does not transfer a finding from one paper to another.
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Source: paper_references, paper_reference_links, observed 2026-08-10T22:40:29.584995Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
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A source-named dated measurement, never combined with another source.
Source: cited_works
94 of 94 outbound references displayed
External citation measurements
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Source-free Semantic Regularization Learning for Semi-supervised Domain Adaptation On mixup regu- larization,
Reference 80
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Observation 9f00bab5-0026-4a65-a656-b01e42b70000 · outbound
Source-free Semantic Regularization Learning for Semi-supervised Domain Adaptation Prevalence of neural collapse during the terminal phase of deep learning training,
Reference 81
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Observation 0148e3c1-df96-453e-9d78-469e253af7ae · outbound
Source-free Semantic Regularization Learning for Semi-supervised Domain Adaptation Proxymix: Proxy-based mixup training with label refinery for source-free domain adaptation,
Reference 82
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Observation 8e26f24a-b10c-4247-8fcc-1a8af627efa1 · outbound
Source-free Semantic Regularization Learning for Semi-supervised Domain Adaptation Understanding and improving early stopping for learning with noisy labels,
Reference 83
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Source-free Semantic Regularization Learning for Semi-supervised Domain Adaptation Prestopping: How does early stopping help generalization against label noise?,
Reference 84
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Observation ea813e69-76f9-490e-943f-1c6254415981 · outbound
Source-free Semantic Regularization Learning for Semi-supervised Domain Adaptation Semi-supervised learning by entropy minimization,
Reference 85
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Observation e96495de-f1d6-44d3-87ba-65135c848109 · outbound
Source-free Semantic Regularization Learning for Semi-supervised Domain Adaptation Clda: Contrastive learning for semi-supervised domain adaptation,
Reference 86
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Observation 333de6d8-ed52-483f-8077-922cef123c59 · outbound
Source-free Semantic Regularization Learning for Semi-supervised Domain Adaptation Moment matching for multi-source domain adaptation,
Reference 87
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Source-free Semantic Regularization Learning for Semi-supervised Domain Adaptation Very Deep Convolutional Networks for Large-Scale Image Recognition
Reference 88
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Observation ad772a01-aa92-49c2-9f3c-1299a256f331 · outbound
Source-free Semantic Regularization Learning for Semi-supervised Domain Adaptation Randaugment: Practical automated data augmentation with a reduced search space,
Reference 89
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Observation 7f2350c1-9bf0-4b8b-a7cc-6a184ad1963c · outbound
Source-free Semantic Regularization Learning for Semi-supervised Domain Adaptation Pytorch: An imperative style, high-performance deep learning library,
Reference 90
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Observation 9cee15e0-6495-43e8-b26e-c9ae01fba68a · outbound
Source-free Semantic Regularization Learning for Semi-supervised Domain Adaptation Visualizing data using t-sne.,
Reference 91
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Observation ccd2f70c-2bfc-4a53-9c6d-1f9d523738d2 · outbound
Source-free Semantic Regularization Learning for Semi-supervised Domain Adaptation Grad-cam: Visual explanations from deep networks via gradient-based localization,
Reference 92
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Observation a47d62b9-150f-4eeb-b114-2f22b84380db · outbound
Source-free Semantic Regularization Learning for Semi-supervised Domain Adaptation Transferability vs. dis- criminability: Batch spectral penalization for adversarial domain adap- tation,
Reference 93
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Observation d0c6a3f9-0faa-48af-985c-ed66fb1d8d44 · outbound
Source-free Semantic Regularization Learning for Semi-supervised Domain Adaptation A collaborative alignment framework of transferable knowledge extraction for unsuper- vised domain adaptation,
Reference 94
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No inbound Pith citation observations are available.