Paper Citation Record · LEDGER

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization

As of 20 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2501.00818.

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

pith.paper-citation-record.v1
2501.00818 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:45:39.312130Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

42 of 42 outbound references displayed

verified exact1
verified fuzzy35
unresolved6
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1dad4500-58df-41bd-9872-961ab07acbd4 · outbound

This paper cites Continual test -time domain adaptation,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Continual test -time domain adaptation,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:40.134502Z

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 58cf1b36-919b-4bf0-aae4-4b47bfd35fb8 · outbound

This paper cites Improving robustness against common corruptions by covariate shift adaptation,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Improving robustness against common corruptions by covariate shift adaptation,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:40.114874Z

Source-reported events for the cited work

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Observation 69b496c8-2124-45a0-a1c8-b0af85693e2b · outbound

This paper cites Tent: Fully Test-time Adaptation by Entropy Minimization.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Tent: Fully Test-time Adaptation by Entropy Minimization

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:39.087063Z

Source-reported events for the cited work

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Observation df45c9cc-c136-403d-9e5d-7b261b685068 · outbound

This paper cites Evaluation of Test-Time Adaptation Under Computational Time Constraints.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Evaluation of Test-Time Adaptation Under Computational Time Constraints

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:45:39.475839Z

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Observation 44662316-aef4-4ed5-9c09-ffc9876d7383 · outbound

This paper cites Efficient test -time model adaptation without forgetting,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Efficient test -time model adaptation without forgetting,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:40.097761Z

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 63abb3dd-5041-458b-b278-b515d3388ee1 · outbound

This paper cites Adversarial continual learning,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Adversarial continual learning,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:40.080986Z

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 7f27f3c2-9fce-4300-a477-0ded336eca98 · outbound

This paper cites Catastrophic interference in connectionist networks: The sequential learning problem,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Catastrophic interference in connectionist networks: The sequential learning problem,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:40.064617Z

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 0f0b7d66-5546-4df0-9d39-22eadc2f4c0f · outbound

This paper cites Continual lifelong learning with neural networks: A review,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Continual lifelong learning with neural networks: A review,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:40.046588Z

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 d8b45c68-7815-43fc-9dc9-395e69c9beda · outbound

This paper cites Instance weighting for domain adaptation in nlp,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Instance weighting for domain adaptation in nlp,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:40.021553Z

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 7aec54ba-07db-4572-88f6-822c2b958662 · outbound

This paper cites Instance weighting for neural machine translation domain adaptation,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Instance weighting for neural machine translation domain adaptation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.995721Z

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 ac578d7b-75c2-4614-90a7-3542efbf119f · outbound

This paper cites Boosting for transfer learning with multiple sources,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Boosting for transfer learning with multiple sources,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.973906Z

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 78eb4cc9-3230-468e-b314-a35b15c2f02b · outbound

This paper cites Domain -adversarial training of neural networks,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Domain -adversarial training of neural networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.955499Z

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 22153af3-8a08-4d21-ad59-c27df51f953b · outbound

This paper cites Adversarial discriminative domain adaptation,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Adversarial discriminative domain adaptation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.936883Z

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 db65d1ab-0672-4a12-803e-6de69c448fa1 · outbound

This paper cites Homm: Higher -order moment matching for unsupervised domain adaptation,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Homm: Higher -order moment matching for unsupervised domain adaptation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.920314Z

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 1e124aa6-6d10-4307-8318-a3fd55a0586f · outbound

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

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Deep coral: Correlation alignment for deep domain adaptation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.897426Z

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 457d969f-b0c8-4685-8783-d95422939068 · outbound

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

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.879486Z

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 293cf98a-5482-4bb0-8598-f3a6d8e90440 · outbound

This paper cites Contrastive adaptation network for unsupervised domain adaptation,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Contrastive adaptation network for unsupervised domain adaptation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.862949Z

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 6844899e-f81c-4308-96dc-49657853828d · outbound

This paper cites Contrastive learning and self -training for unsupervised domain adaptation in semantic segmentation,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Contrastive learning and self -training for unsupervised domain adaptation in semantic segmentation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.847241Z

Source-reported events for the cited work

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Observation 65b53cef-238d-4410-815c-d8fe97837b37 · outbound

This paper cites Parameter-free online test -time adaptation,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Parameter-free online test -time adaptation,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.830632Z

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 bb611b63-5dcf-43a0-ad2a-79638e059c24 · outbound

This paper cites Overcoming catastrophic forgetting in neural networks,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Overcoming catastrophic forgetting in neural networks,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.814203Z

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 f84e7b53-45dc-47db-8137-8e94665a530c · outbound

This paper cites Towards Stable Test-Time Adaptation in Dynamic Wild World.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Towards Stable Test-Time Adaptation in Dynamic Wild World

Reference 21

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Source-reported events for the cited work

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Observation 7edf9f57-df21-4fd6-bdde-ae6b65ac7a45 · outbound

This paper cites Robust mean teacher for continual and gradual test-time adaptation,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Robust mean teacher for continual and gradual test-time adaptation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.798353Z

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 70ad1650-4c97-4e1f-aa2d-9d3856aa29d9 · outbound

This paper cites Improving test -time adaptation via shif t-agnostic weight regularization and nearest source prototypes,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Improving test -time adaptation via shif t-agnostic weight regularization and nearest source prototypes,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.782353Z

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 9587137a-846a-42b4-aaa9-9878100d8f6c · outbound

This paper cites Ar -tta: A simple method for real -world continual test -time adap tation,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Ar -tta: A simple method for real -world continual test -time adap tation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.766706Z

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 48b29e4e-944f-4e39-9fb6-0c315620f2d5 · outbound

This paper cites Robust test -time adaptation in dynamic scenarios,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Robust test -time adaptation in dynamic scenarios,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.750343Z

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 6465bf6d-53f9-4f48-8852-a75218aab91b · outbound

This paper cites A continual learning survey: Defying forgetting in classification tasks,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization A continual learning survey: Defying forgetting in classification tasks,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.732319Z

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 f58b19a9-c883-4394-a563-32db685601f7 · outbound

This paper cites icarl: Incremental classifier and representation learning,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization icarl: Incremental classifier and representation learning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.715612Z

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 76adb5a8-3a36-42a8-8047-4a64e03b5cc4 · outbound

This paper cites The task rehearsal method of life-long learning: Overcoming impoverished data,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization The task rehearsal method of life-long learning: Overcoming impoverished data,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.699134Z

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 c23e4169-9938-4820-babb-0167576bf819 · outbound

This paper cites Continual learning through synaptic intelligence,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Continual learning through synaptic intelligence,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.682062Z

Source-reported events for the cited work

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Observation 2c3ee222-4b3b-40f3-a2ab-2815d67ce3f2 · outbound

This paper cites Learning without forgetting,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Learning without forgetting,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.665392Z

Source-reported events for the cited work

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Observation 43a021b9-708f-47fe-82b4-d200fd4446a8 · outbound

This paper cites Memory aware synapses: Learning what (not) to forget,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Memory aware synapses: Learning what (not) to forget,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.648107Z

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 86f23b06-733d-46c8-8459-478a255c6bd3 · outbound

This paper cites Domainadaptor: A novel approach to test-time adaptation,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Domainadaptor: A novel approach to test-time adaptation,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.630413Z

Source-reported events for the cited work

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Observation 4a1c6058-5d79-4847-a69f-2726f1275902 · outbound

This paper cites Reducing the Teacher-Student Gap via Spherical Knowledge Disitllation.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Reducing the Teacher-Student Gap via Spherical Knowledge Disitllation

Reference 33

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Observation 8103ca20-278f-4a8d-9517-d0385eedae7a · outbound

This paper cites Temporal Ensembling for Semi-Supervised Learning.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Temporal Ensembling for Semi-Supervised Learning

Reference 34

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Source-reported events for the cited work

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Observation 686ab0af-d1a2-4d05-9314-561b17e6cd68 · outbound

This paper cites Mean teachers are better role models: Weight -averaged consistency targets improve semi-supervised deep learning results,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Mean teachers are better role models: Weight -averaged consistency targets improve semi-supervised deep learning results,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.613023Z

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 060e285f-fd13-4091-9519-c282bb727426 · outbound

This paper cites Unsupervised data augmentation for consistency training,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Unsupervised data augmentation for consistency training,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.593776Z

Source-reported events for the cited work

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Observation 13d28132-50a6-49eb-a01c-9c387aa43654 · outbound

This paper cites Virtual adversarial training: a regularization method for supervised and semi-supervised learning,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Virtual adversarial training: a regularization method for supervised and semi-supervised learning,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.576614Z

Source-reported events for the cited work

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Observation 81ee1e95-223a-4e5c-84de-d4afcb584b5f · outbound

This paper cites A survey on deep semi-supervised learning,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization A survey on deep semi-supervised learning,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.559083Z

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 200abd29-0ffd-48d9-a341-96035b9cd913 · outbound

This paper cites Symmetric cross entropy for robust learning with noisy labels,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Symmetric cross entropy for robust learning with noisy labels,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.538669Z

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 4202faee-1e0c-40b7-bb3a-d33270dcc6a0 · outbound

This paper cites Wide Residual Networks.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Wide Residual Networks

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:39.301293Z

Source-reported events for the cited work

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Observation 2350aa27-5838-4ec7-a110-cb457b769072 · outbound

This paper cites Aggregated residual transformations for deep neural networks,.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization Aggregated residual transformations for deep neural networks,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:39.519170Z

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 04ebe2d0-1d8f-49e5-84ce-c5dcd82fc9e0 · outbound

This paper cites RobustBench: a standardized adversarial robustness benchmark.

SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization RobustBench: a standardized adversarial robustness benchmark

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:39.312130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.