Paper Citation Record · LEDGER

Random Registers for Cross-Domain Few-Shot Learning

As of 8 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 2 inbound Pith citation observations for arXiv:2506.02843.

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

pith.paper-citation-record.v1
2506.02843 v1

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:20:58.860481Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T22:28:35.155099Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T22:34:01.822775Z

Reference resolution

58 of 58 outbound references displayed

verified exact0
verified fuzzy16
unresolved41
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation af4d670d-994f-442b-902d-3e7e3108499b · outbound

This paper cites write newline.

Random Registers for Cross-Domain Few-Shot Learning write newline

Reference 1

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Source-reported events for the cited work

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Observation 66a63f22-4bc0-4ee4-9b51-150dec2a919f · outbound

This paper cites Accumulated trivial attention matters in vision transformers on small datasets.

Random Registers for Cross-Domain Few-Shot Learning Accumulated trivial attention matters in vision transformers on small datasets

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:20:59.580344Z

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Observation d8ec8338-aff6-406b-bee0-c99dce8a7db5 · outbound

This paper cites On separate normalization in self-supervised transformers, 2023 b.

Random Registers for Cross-Domain Few-Shot Learning On separate normalization in self-supervised transformers, 2023 b

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:20:59.571086Z

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Observation 87461e24-3871-4996-ad4b-d68ae7b5fdd1 · outbound

This paper cites Meta-baseline: Exploring simple meta-learning for few-shot learning, 2021.

Random Registers for Cross-Domain Few-Shot Learning Meta-baseline: Exploring simple meta-learning for few-shot learning, 2021

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:20:59.562181Z

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Observation 24010e01-8737-45a1-8407-200ae919912d · outbound

This paper cites E., Dusza, S., Gutman, D., Helba, B., Kalloo, A., Liopyris, K., Marchetti, M., Kittler, H., and Halpern, A.

Random Registers for Cross-Domain Few-Shot Learning E., Dusza, S., Gutman, D., Helba, B., Kalloo, A., Liopyris, K., Marchetti, M., Kittler, H., and Halpern, A

Reference 5

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Source-reported events for the cited work

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Observation 1a63f55e-e48f-463c-b35a-ce827043f88e · outbound

This paper cites Vision transformers need registers.

Random Registers for Cross-Domain Few-Shot Learning Vision transformers need registers

Reference 6

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Observation 29d1fb0b-a952-422f-8931-628adba57a18 · outbound

This paper cites Confess: A framework for single source cross-domain few-shot learning.

Random Registers for Cross-Domain Few-Shot Learning Confess: A framework for single source cross-domain few-shot learning

Reference 7

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Observation 547861d4-a844-48c6-a02c-7db4917d3837 · outbound

This paper cites Reliability of cka as a similarity measure in deep learning, 2022.

Random Registers for Cross-Domain Few-Shot Learning Reliability of cka as a similarity measure in deep learning, 2022

Reference 8

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Observation a442ff2b-7341-4571-9375-9d01dde78903 · outbound

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

Random Registers for Cross-Domain Few-Shot Learning Imagenet: A large-scale hierarchical image database

Reference 9

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Observation ba49c06d-d5bf-41a2-80c7-099fb9eb3240 · outbound

This paper cites Sharpness-aware minimization for efficiently improving generalization, 2021.

Random Registers for Cross-Domain Few-Shot Learning Sharpness-aware minimization for efficiently improving generalization, 2021

Reference 10

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Observation c2ae84fe-1201-4d61-93a7-a4dc7ddd5040 · outbound

This paper cites Meta-fdmixup: Cross-domain few-shot learning guided by labeled target data.

Random Registers for Cross-Domain Few-Shot Learning Meta-fdmixup: Cross-domain few-shot learning guided by labeled target data

Reference 11

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Observation bb3dcb6b-c1af-4384-beed-dfa262c1764b · outbound

This paper cites Wave-san: Wavelet based style augmentation network for cross-domain few-shot learning, 2022.

Random Registers for Cross-Domain Few-Shot Learning Wave-san: Wavelet based style augmentation network for cross-domain few-shot learning, 2022

Reference 12

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Observation a4380a2c-74bf-4bf5-a5f8-e12ab6dea077 · outbound

This paper cites Styleadv: Meta style adversarial training for cross-domain few-shot learning, 2023.

Random Registers for Cross-Domain Few-Shot Learning Styleadv: Meta style adversarial training for cross-domain few-shot learning, 2023

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T11:20:58.719073Z

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Observation 4d9de87c-6df2-4983-a08d-d4b5625c0b00 · outbound

This paper cites C., Karlinsky, L., Codella, J.

Random Registers for Cross-Domain Few-Shot Learning C., Karlinsky, L., Codella, J

Reference 14

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Observation 7bd1cef3-c2e1-4c74-a7f4-5606bd90f0b2 · outbound

This paper cites Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification, 2019.

Random Registers for Cross-Domain Few-Shot Learning Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification, 2019

Reference 15

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Observation 009f90de-c117-4a19-b0f9-d775c6421860 · outbound

This paper cites and Ma, A.

Random Registers for Cross-Domain Few-Shot Learning and Ma, A

Reference 16

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Observation 36619385-5b50-4326-a932-a6007eec7f54 · outbound

This paper cites Visual prompt tuning.

Random Registers for Cross-Domain Few-Shot Learning Visual prompt tuning

Reference 17

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Observation c8ebdb9f-d3e3-415d-b388-a48bacce434b · outbound

This paper cites and Han, B.

Random Registers for Cross-Domain Few-Shot Learning and Han, B

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:20:59.483945Z

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Observation c2ff2a53-d1c8-4bbd-944e-159655b34d98 · outbound

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Random Registers for Cross-Domain Few-Shot Learning Unresolved cited work

Reference 19

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Observation 47f44b2b-3c5a-4e31-82a5-7c1047c45edf · outbound

This paper cites Similarity of neural network representations revisited.

Random Registers for Cross-Domain Few-Shot Learning Similarity of neural network representations revisited

Reference 20

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Observation fd1d393c-9268-439f-8822-f9f999827399 · outbound

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Random Registers for Cross-Domain Few-Shot Learning Unresolved cited work

Reference 21

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Observation 6ebb6dd1-9e14-437a-9545-f6e2135261b9 · outbound

This paper cites Adversarial feature hallucination networks for few-shot learning.

Random Registers for Cross-Domain Few-Shot Learning Adversarial feature hallucination networks for few-shot learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:20:59.459983Z

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Observation 197857bb-8332-4b3e-aa91-a5dbdd50c327 · outbound

This paper cites Ranking distance calibration for cross-domain few-shot learning, 2022.

Random Registers for Cross-Domain Few-Shot Learning Ranking distance calibration for cross-domain few-shot learning, 2022

Reference 23

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Observation 32b338af-d5fe-4773-b9e8-c1f397b56432 · outbound

This paper cites Learning multi-level weight-centric features for few-shot learning.

Random Registers for Cross-Domain Few-Shot Learning Learning multi-level weight-centric features for few-shot learning

Reference 24

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Observation d1a813ea-5605-4379-af5f-0d841ea115ab · outbound

This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.

Random Registers for Cross-Domain Few-Shot Learning Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:20:59.444488Z

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Observation 8b9f9208-4582-4036-a3be-9093d04ba146 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Random Registers for Cross-Domain Few-Shot Learning Swin transformer: Hierarchical vision transformer using shifted windows

Reference 26

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Observation df2303d5-507c-498c-88b3-031c071eb3b3 · outbound

This paper cites Reconstruction Target Matters in Masked Image Modeling for Cross-Domain Few-Shot Learning.

Random Registers for Cross-Domain Few-Shot Learning Reconstruction Target Matters in Masked Image Modeling for Cross-Domain Few-Shot Learning

Reference 27

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Observation ff937e1a-d806-4aa2-a1b0-ee19e2072640 · outbound

This paper cites Prod: Prompting-to-disentangle domain knowledge for cross-domain few-shot image classification.

Random Registers for Cross-Domain Few-Shot Learning Prod: Prompting-to-disentangle domain knowledge for cross-domain few-shot image classification

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:20:59.430275Z

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Observation 0ed63c6a-f9c2-4320-a671-8e7f994ed73c · outbound

This paper cites Using deep learning for image-based plant disease detection.

Random Registers for Cross-Domain Few-Shot Learning Using deep learning for image-based plant disease detection

Reference 29

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Observation 90e0905d-d5e6-48ac-981c-4d49eba862d7 · outbound

This paper cites M., Ranasinghe, K., Khan, S.

Random Registers for Cross-Domain Few-Shot Learning M., Ranasinghe, K., Khan, S

Reference 30

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Random Registers for Cross-Domain Few-Shot Learning A., Osowiechi, D., Ayed, I

Reference 31

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Observation 5b951dd6-b809-4a74-a748-cc6d1e3c6338 · outbound

This paper cites Understanding cross-domain few-shot learning based on domain similarity and few-shot difficulty, 2022.

Random Registers for Cross-Domain Few-Shot Learning Understanding cross-domain few-shot learning based on domain similarity and few-shot difficulty, 2022

Reference 32

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This paper cites W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.

Random Registers for Cross-Domain Few-Shot Learning W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al

Reference 33

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Random Registers for Cross-Domain Few-Shot Learning Unresolved cited work

Reference 34

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This paper cites Prototypical networks for few-shot learning.

Random Registers for Cross-Domain Few-Shot Learning Prototypical networks for few-shot learning

Reference 35

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Random Registers for Cross-Domain Few-Shot Learning Visual prompt tuning for generative transfer learning

Reference 36

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This paper cites Cross-domain few-shot classification via learned feature-wise transformation.

Random Registers for Cross-Domain Few-Shot Learning Cross-domain few-shot classification via learned feature-wise transformation

Reference 37

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Random Registers for Cross-Domain Few-Shot Learning Matching networks for one shot learning

Reference 38

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Random Registers for Cross-Domain Few-Shot Learning Unresolved cited work

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Random Registers for Cross-Domain Few-Shot Learning and Deng, Z.-H

Reference 40

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Random Registers for Cross-Domain Few-Shot Learning Z., and Yan, S

Reference 41

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Observation 6dc0b10b-a455-4a09-ab26-76e8ceb36d1d · outbound

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Random Registers for Cross-Domain Few-Shot Learning Unresolved cited work

Reference 42

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Random Registers for Cross-Domain Few-Shot Learning Efficient vision-language pre-training by cluster masking

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:20:59.360382Z

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Observation 6864de90-32e8-48f8-a72d-84d494aa9a54 · outbound

This paper cites A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT.

Random Registers for Cross-Domain Few-Shot Learning A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

Reference 44

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Random Registers for Cross-Domain Few-Shot Learning Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey

Reference 45

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Random Registers for Cross-Domain Few-Shot Learning Enhancing information maximization with distance-aware contrastive learning for source-free cross-domain few-shot learning

Reference 46

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Random Registers for Cross-Domain Few-Shot Learning Visual-language prompt tuning with knowledge-guided context optimization

Reference 47

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Observation 239972cc-d9cd-43df-87e1-33f856bb8b55 · outbound

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Random Registers for Cross-Domain Few-Shot Learning Delving deep into the generalization of vision transformers under distribution shifts

Reference 48

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Random Registers for Cross-Domain Few-Shot Learning M., and Shum, H.-Y

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:20:59.335895Z

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Observation b1d0ea69-3b52-4a6d-bac6-10876c266b9e · outbound

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Random Registers for Cross-Domain Few-Shot Learning Free-lunch for cross-domain few-shot learning: Style-aware episodic training with robust contrastive learning

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:20:59.326920Z

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Observation c145120d-ed54-42d2-bc2a-8e681d523085 · outbound

This paper cites Revisiting prototypical network for cross domain few-shot learning.

Random Registers for Cross-Domain Few-Shot Learning Revisiting prototypical network for cross domain few-shot learning

Reference 51

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Random Registers for Cross-Domain Few-Shot Learning iBOT: Image BERT Pre-Training with Online Tokenizer

Reference 52

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Random Registers for Cross-Domain Few-Shot Learning Attention temperature matters in vit-based cross-domain few-shot learning

Reference 53

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Observation 1f4701d7-5d01-4eda-8f02-eff5b76ae42a · outbound

This paper cites A closer look at the cls token for cross-domain few-shot learning.

Random Registers for Cross-Domain Few-Shot Learning A closer look at the cls token for cross-domain few-shot learning

Reference 54

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Observation 8f3718d2-39a9-48f3-b28e-af5e4eeed244 · outbound

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Random Registers for Cross-Domain Few-Shot Learning Unresolved cited work

Reference 55

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Observation d00705e5-7c8f-4d0f-89c6-688e4e5d4dfa · outbound

This paper cites Margin-based few-shot class-incremental learning with class-level overfitting mitigation.

Random Registers for Cross-Domain Few-Shot Learning Margin-based few-shot class-incremental learning with class-level overfitting mitigation

Reference 56

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This paper cites Flatten long-range loss landscapes for cross-domain few-shot learning, 2024 a.

Random Registers for Cross-Domain Few-Shot Learning Flatten long-range loss landscapes for cross-domain few-shot learning, 2024 a

Reference 57

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This paper cites Compositional Few-Shot Class-Incremental Learning.

Random Registers for Cross-Domain Few-Shot Learning Compositional Few-Shot Class-Incremental Learning

Reference 58

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Addressing Exacerbated Attention Sink for Source-Free Cross-Domain Few-Shot Learning cites this paper.

Addressing Exacerbated Attention Sink for Source-Free Cross-Domain Few-Shot Learning Random Registers for Cross-Domain Few-Shot Learning

Reference 34

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Improving CLIP Adaptation by Breaking Tail Alignment for Source-Free Cross-Domain Few-Shot Learning cites this paper.

Improving CLIP Adaptation by Breaking Tail Alignment for Source-Free Cross-Domain Few-Shot Learning Random Registers for Cross-Domain Few-Shot Learning

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