Paper Citation Record · LEDGER

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning

As of 16 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2507.23237.

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

pith.paper-citation-record.v1
2507.23237 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T10:59:37.636008Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

32 of 32 outbound references displayed

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verified fuzzy31
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4aadfe96-9acb-4e4b-89c5-b86c79d7c9a9 · outbound

This paper cites Exploring example influence in continual learning,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Exploring example influence in continual learning,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:41.066297Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e1763546-057f-403a-92a1-ae7811016136 · outbound

This paper cites Multi-domain multi-task rehearsal for lifelong learning,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Multi-domain multi-task rehearsal for lifelong learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:41.051213Z

Source-reported events for the cited work

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Observation bfaa5593-87f5-4b8e-a4b1-affec146c871 · outbound

This paper cites Harnessing multi-semantic hypergraph for few-shot learning,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Harnessing multi-semantic hypergraph for few-shot learning,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:41.036544Z

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Observation 539f26d0-000b-4b11-94b4-55dc01d72c72 · outbound

This paper cites Measuring asymmetric gradient discrepancy in parallel continual learning,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Measuring asymmetric gradient discrepancy in parallel continual learning,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:41.021163Z

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Observation 672eccda-ef74-4009-9a21-e935fbaf998e · outbound

This paper cites Multi-semantic hypergraph neural network for effective few- shot learning,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Multi-semantic hypergraph neural network for effective few- shot learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:41.006353Z

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Observation c93f7c06-6603-46eb-a77f-ff2ebdacc818 · outbound

This paper cites Metamask: Improving few- shot semantic segmentation via multi-mask calibration,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Metamask: Improving few- shot semantic segmentation via multi-mask calibration,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:40.989606Z

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Observation ab123aaa-3d00-43e7-83a9-cf537ec3b784 · outbound

This paper cites Safe: Slow and fast parameter-efficient tuning for continual learning with pre-trained models,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Safe: Slow and fast parameter-efficient tuning for continual learning with pre-trained models,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:40.974580Z

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Observation e84cafb5-5cb2-4232-8949-199fc43e1687 · outbound

This paper cites Few-shot class-incremental learning,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Few-shot class-incremental learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:40.959341Z

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Observation 04296ed1-edba-4ab9-a7a0-25ef4bfb1dd8 · outbound

This paper cites Few-shot incre- mental learning with continually evolved classifiers,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Few-shot incre- mental learning with continually evolved classifiers,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:40.945327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 975d6e93-be3d-4643-b4bf-aa5b2414969c · outbound

This paper cites For- ward compatible few-shot class-incremental learning,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning For- ward compatible few-shot class-incremental learning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:40.931378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e7ab9079-9ad7-4bc1-a8d8-929ed4fe8745 · outbound

This paper cites Memorizing complementation network for few-shot class-incremental learning,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Memorizing complementation network for few-shot class-incremental learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:40.916836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f97e435d-27c8-4819-956b-01749f36657b · outbound

This paper cites Few- shot class-incremental learning via class-aware bilateral distillation,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Few- shot class-incremental learning via class-aware bilateral distillation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:40.902197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 88f11dc1-28e8-4cbf-9a22-5a7cd0694522 · outbound

This paper cites CALA: A Class-Aware Logit Adapter for Few-Shot Class-Incremental Learning.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning CALA: A Class-Aware Logit Adapter for Few-Shot Class-Incremental Learning

Reference 13

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Observation d94bebb2-7cba-4456-bdff-511340f1ecd1 · outbound

This paper cites A strong baseline for semi-supervised incremental few-shot learning,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning A strong baseline for semi-supervised incremental few-shot learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:40.888135Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3aee68bd-7f25-4dda-9b7b-cadb8e04b597 · outbound

This paper cites Uncertainty-aware distillation for semi-supervised few-shot class-incremental learning,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Uncertainty-aware distillation for semi-supervised few-shot class-incremental learning,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:40.873694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b7646b72-af9a-4878-a53c-e7831fc27b61 · outbound

This paper cites Semi-supervised few- shot class-incremental learning,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Semi-supervised few- shot class-incremental learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:40.859345Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 15c3344e-a269-4831-8a75-6d55cb441795 · outbound

This paper cites Uncertainty-guided semi-supervised few-shot class-incremental learn- ing with knowledge distillation,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Uncertainty-guided semi-supervised few-shot class-incremental learn- ing with knowledge distillation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:40.845284Z

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Observation b009c355-09bf-4bc6-9bc1-cb2b8196e792 · outbound

This paper cites Learning with fantasy: Semantic-aware virtual contrastive constraint for few-shot class- incremental learning,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Learning with fantasy: Semantic-aware virtual contrastive constraint for few-shot class- incremental learning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:40.831260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d3fdab86-b752-4ba9-92ec-d3fcbb7448ef · outbound

This paper cites Few- shot class-incremental learning by sampling multi-phase tasks,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Few- shot class-incremental learning by sampling multi-phase tasks,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:40.812263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fc7d0050-5d5b-4a84-ac55-e414abebe643 · outbound

This paper cites Multi-feature space similarity supplement for few-shot class incremental learning,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Multi-feature space similarity supplement for few-shot class incremental learning,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:40.624528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fada0b2d-f0c1-4c9a-8ad1-fc2c5fd9e426 · outbound

This paper cites Self-promoted prototype refinement for few-shot class-incremental learning,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Self-promoted prototype refinement for few-shot class-incremental learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:40.422929Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bee7aad9-7b24-4cbd-981e-1c453b9db4f1 · outbound

This paper cites Few-shot class-incremental learning via asymmetric supervised contrastive learn- ing,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Few-shot class-incremental learning via asymmetric supervised contrastive learn- ing,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:40.192878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ea3aff9b-179d-422c-9845-91c38738e005 · outbound

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

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Virtual adversarial training: a regularization method for supervised and semi-supervised learning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:39.963829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 82ab9c91-363d-4a47-8771-9c3c8f4261be · outbound

This paper cites Mixmatch: A holistic approach to semi-supervised learning,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Mixmatch: A holistic approach to semi-supervised learning,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:39.813921Z

Source-reported events for the cited work

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Observation abca5696-8c98-4fbc-96de-09b85b91ebaa · outbound

This paper cites Transductive semi- supervised deep learning using min-max features,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Transductive semi- supervised deep learning using min-max features,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:39.625228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 06370cc9-8d27-4822-8912-7af19ded8e7c · outbound

This paper cites Transductive learning via spectral graph partitioning,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Transductive learning via spectral graph partitioning,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:39.487292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8db1c641-ab40-4de7-b806-4f7bcc3effa0 · outbound

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

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:39.282817Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1df682e1-e20d-435e-bb62-c701d240bbd6 · outbound

This paper cites Label propagation for deep semi-supervised learning,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Label propagation for deep semi-supervised learning,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:39.125901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4e2638cc-edb9-4af1-895b-8b35ba1a8c1a · outbound

This paper cites Transductive inference for text classification using support vector machines,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Transductive inference for text classification using support vector machines,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:38.934240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 94e2793b-a287-4862-8474-a6f1be7f4df1 · outbound

This paper cites Free lunch for few-shot learning: Distribution calibration,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Free lunch for few-shot learning: Distribution calibration,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:38.767487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 43f7223a-e5e2-44f3-a1a6-1ff282262f06 · outbound

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

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning icarl: Incremental classifier and representation learning,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:38.428427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2acb4edb-6764-4175-9cf5-cf4be2f5f8d9 · outbound

This paper cites Visualizing data using t-sne,.

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning Visualizing data using t-sne,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:59:38.141609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

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