Paper Citation Record · LEDGER

Leveraging Lightweight Generators for Memory Efficient Continual Learning

As of 21 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2506.19692.

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

pith.paper-citation-record.v1
2506.19692 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:33:24.569679Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

30 of 30 outbound references displayed

verified exact1
verified fuzzy17
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c486eeb0-bf20-4122-911a-52792115c0b4 · outbound

This paper cites Thrun,Lifelong Learning Algorithms, pp.

Leveraging Lightweight Generators for Memory Efficient Continual Learning Thrun,Lifelong Learning Algorithms, pp

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:33:26.120961Z

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Observation 9e47c042-5c2b-47c4-855a-1fee2df83dd7 · outbound

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

Leveraging Lightweight Generators for Memory Efficient Continual Learning Continual lifelong learning with neural networks: A review,

Reference 2

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Source-reported events for the cited work

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Observation 0551240b-1f08-4898-bf02-8e7d35849183 · outbound

This paper cites Three scenarios for continual learning.

Leveraging Lightweight Generators for Memory Efficient Continual Learning Three scenarios for continual learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T18:33:23.884774Z

Source-reported events for the cited work

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Observation 6f02d05d-793b-4036-bd3a-e1e9a492cf0a · outbound

This paper cites The end of moore’s law: A new beginning for information technology,.

Leveraging Lightweight Generators for Memory Efficient Continual Learning The end of moore’s law: A new beginning for information technology,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T18:33:23.890426Z

Source-reported events for the cited work

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Observation 9fef1fb5-be2a-4d5e-9ef5-caa158da4917 · outbound

This paper cites Singular value decomposition and least squares solutions,.

Leveraging Lightweight Generators for Memory Efficient Continual Learning Singular value decomposition and least squares solutions,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:33:25.977166Z

Source-reported events for the cited work

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Observation 590e0a79-797b-4768-9353-7c3f08c50819 · outbound

This paper cites A comprehensive survey of continual learning: theory, method and application,.

Leveraging Lightweight Generators for Memory Efficient Continual Learning A comprehensive survey of continual learning: theory, method and application,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:33:25.880156Z

Source-reported events for the cited work

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Observation ded26212-1261-46e5-8651-d232bc0ec567 · outbound

This paper cites Continual unsupervised representation learning,.

Leveraging Lightweight Generators for Memory Efficient Continual Learning Continual unsupervised representation learning,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T18:33:24.020833Z

Source-reported events for the cited work

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Observation 86296585-aa65-4e3f-9ffb-a9362c1d1a41 · outbound

This paper cites On Tiny Episodic Memories in Continual Learning.

Leveraging Lightweight Generators for Memory Efficient Continual Learning On Tiny Episodic Memories in Continual Learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T18:33:24.042689Z

Source-reported events for the cited work

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Observation 6e0ed40c-8c32-4ada-ae7c-4ead07b17f8f · outbound

This paper cites Clustering- based domain-incremental learning,.

Leveraging Lightweight Generators for Memory Efficient Continual Learning Clustering- based domain-incremental learning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:33:25.855398Z

Source-reported events for the cited work

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Observation 240d8104-12af-400e-b8c0-a676593e10da · outbound

This paper cites Continual learning with deep generative replay,.

Leveraging Lightweight Generators for Memory Efficient Continual Learning Continual learning with deep generative replay,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:33:25.839769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 581ac2d8-39c5-4017-bef2-14deeffa7721 · outbound

This paper cites Generative replay with feedback connections as a general strategy for continual learning.

Leveraging Lightweight Generators for Memory Efficient Continual Learning Generative replay with feedback connections as a general strategy for continual learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T18:33:24.058009Z

Source-reported events for the cited work

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Observation 1039ba61-b027-41fa-9db4-a01a1b5fb9c0 · outbound

This paper cites The hippocampal formation as a hierarchical gener- ative model supporting generative replay and continual learning,.

Leveraging Lightweight Generators for Memory Efficient Continual Learning The hippocampal formation as a hierarchical gener- ative model supporting generative replay and continual learning,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:33:25.809047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a2656a2f-294a-46cb-a39f-3dcd6c94b008 · outbound

This paper cites Generative mod- els from the perspective of continual learning,.

Leveraging Lightweight Generators for Memory Efficient Continual Learning Generative mod- els from the perspective of continual learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:33:25.640146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 71bbe0dc-1c37-46ad-9deb-0b36f4a6a135 · outbound

This paper cites Brain-inspired replay for continual learning with artificial neural networks,.

Leveraging Lightweight Generators for Memory Efficient Continual Learning Brain-inspired replay for continual learning with artificial neural networks,

Reference 14

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Source-reported events for the cited work

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Observation b4eaa2e9-d741-4e89-b07e-c61491cd28f6 · outbound

This paper cites Reducing Catastrophic Forgetting in Online Class Incremental Learning Using Self-Distillation.

Leveraging Lightweight Generators for Memory Efficient Continual Learning Reducing Catastrophic Forgetting in Online Class Incremental Learning Using Self-Distillation

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-15T18:33:24.785100Z

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Observation c8d298df-ff7d-45a1-a548-344a563ff388 · outbound

This paper cites Orthogonal gradient descent for contin- ual learning,.

Leveraging Lightweight Generators for Memory Efficient Continual Learning Orthogonal gradient descent for contin- ual learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:33:25.562297Z

Source-reported events for the cited work

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Observation 0263216e-e3cf-4167-9c3b-b7f7c5c87bcc · outbound

This paper cites Gradient Projection Memory for Continual Learning.

Leveraging Lightweight Generators for Memory Efficient Continual Learning Gradient Projection Memory for Continual Learning

Reference 17

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Source-reported events for the cited work

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Observation 043aaae8-74ac-46ee-961d-81465b698c69 · outbound

This paper cites Efficient Lifelong Learning with A-GEM.

Leveraging Lightweight Generators for Memory Efficient Continual Learning Efficient Lifelong Learning with A-GEM

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T18:33:24.243874Z

Source-reported events for the cited work

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Observation 0c38029a-c992-46e1-84f4-adf65fbcfb80 · outbound

This paper cites Experience replay for con- tinual learning,.

Leveraging Lightweight Generators for Memory Efficient Continual Learning Experience replay for con- tinual learning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:33:25.548412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cb8d5399-462b-4e95-9108-fe38392a527e · outbound

This paper cites Rethinking experience replay: a bag of tricks for continual learning,.

Leveraging Lightweight Generators for Memory Efficient Continual Learning Rethinking experience replay: a bag of tricks for continual learning,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:33:25.358975Z

Source-reported events for the cited work

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Observation 32e9d338-e591-4e76-9975-b4c3f0144aa6 · outbound

This paper cites Tutorial: Complexity analysis of Singular Value Decomposition and its variants.

Leveraging Lightweight Generators for Memory Efficient Continual Learning Tutorial: Complexity analysis of Singular Value Decomposition and its variants

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T18:33:24.389291Z

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Observation 8e858945-9a27-4f73-b6e9-b8a82096a61f · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Leveraging Lightweight Generators for Memory Efficient Continual Learning Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T18:33:24.435713Z

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Observation 6d3b5840-76ba-49e0-8566-f32906cd0bef · outbound

This paper cites The mnist database of handwritten digit images for machine learning research,.

Leveraging Lightweight Generators for Memory Efficient Continual Learning The mnist database of handwritten digit images for machine learning research,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T18:33:24.455120Z

Source-reported events for the cited work

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Observation 9420b80a-f40c-4afc-90b8-f4a8391b8166 · outbound

This paper cites Not mnist,.

Leveraging Lightweight Generators for Memory Efficient Continual Learning Not mnist,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:33:25.276095Z

Source-reported events for the cited work

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Observation 34f3025f-ba42-4d9f-acd5-78643b87fdd4 · outbound

This paper cites Cifar10,.

Leveraging Lightweight Generators for Memory Efficient Continual Learning Cifar10,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:33:25.259490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cd7e3ed6-30b9-49e8-9ecb-671bad05c887 · outbound

This paper cites Reading digits in natural images with unsupervised feature learning,.

Leveraging Lightweight Generators for Memory Efficient Continual Learning Reading digits in natural images with unsupervised feature learning,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:33:25.115550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4339814d-dd7b-4d11-b666-b3f74e4a401e · outbound

This paper cites Multilayer perceptron and neural networks,.

Leveraging Lightweight Generators for Memory Efficient Continual Learning Multilayer perceptron and neural networks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:33:25.021845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6ed04409-f811-4607-87b4-3cb2f9c05851 · outbound

This paper cites Multilayer perceptrons,.

Leveraging Lightweight Generators for Memory Efficient Continual Learning Multilayer perceptrons,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:33:25.004851Z

Source-reported events for the cited work

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Observation 6267b887-cdc3-46cf-baf9-8198a8bd6f37 · outbound

This paper cites Mlp-mixer: An all-mlp architecture for vision,.

Leveraging Lightweight Generators for Memory Efficient Continual Learning Mlp-mixer: An all-mlp architecture for vision,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:33:24.988927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9c9cbcbb-3e4d-4dea-af0b-0e5bee517834 · outbound

This paper cites Deep residual learning for image recognition,.

Leveraging Lightweight Generators for Memory Efficient Continual Learning Deep residual learning for image recognition,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:33:24.961136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.