Paper Citation Record · LEDGER

Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning

As of 10 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2505.20135.

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

pith.paper-citation-record.v1
2505.20135 v2

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:06:49.372974Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

18 of 18 outbound references displayed

verified exact2
verified fuzzy5
unresolved10
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0e32ffda-7a39-4e2a-9fb8-7a6913377d97 · outbound

This paper cites Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System.

Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T14:06:48.007640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3493bfe3-6914-4727-9b51-8b3bfa7e7fe3 · outbound

This paper cites dataset was partitioned into 10 tasks, each with 20 classes, and its test set contains 10,000 images, referred to as Split Tiny-ImageNet.

Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning dataset was partitioned into 10 tasks, each with 20 classes, and its test set contains 10,000 images, referred to as Split Tiny-ImageNet

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:06:50.298194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b26a20b2-feaf-4474-b1f5-8c8ce0c53e7b · outbound

This paper cites Connectionist models of recognition memory: constraints imposed by learning and forgetting functions.

Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning Connectionist models of recognition memory: constraints imposed by learning and forgetting functions

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:06:50.940055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bf50861e-8a76-4b85-9b84-756b3cfac588 · outbound

This paper cites Singular Value Fine-tuning for Few-Shot Class-Incremental Learning.

Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning Singular Value Fine-tuning for Few-Shot Class-Incremental Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T14:06:48.654133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42d3183e-211a-4f99-a46a-2b0b27a38c7d · outbound

This paper cites an unresolved cited work.

Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning Unresolved cited work

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T14:06:48.736549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3edda407-83b3-4033-80d9-bd389a58ca47 · outbound

This paper cites Continual Learning for Segment Anything Model Adaptation.

Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning Continual Learning for Segment Anything Model Adaptation

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:06:49.700798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9ce17eac-2fb6-481e-95d2-a82e85c84fb5 · outbound

This paper cites Online Coreset Selection for Rehearsal-based Continual Learning.

Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning Online Coreset Selection for Rehearsal-based Continual Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T14:06:48.863915Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0b0bafb-b652-48da-9cd1-e9442d91dc7d · outbound

This paper cites Zhang, L., Zhang, J., Lei, B., Mukherjee, S., Pan, X., Zhao, B., Ding, C., Li, Y ., and Xu, D.

Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning Zhang, L., Zhang, J., Lei, B., Mukherjee, S., Pan, X., Zhao, B., Ding, C., Li, Y ., and Xu, D

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T14:06:49.008108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79fb4303-3b46-49f2-8fc0-087bae34ee01 · outbound

This paper cites Dataset Condensation with Gradient Matching.

Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning Dataset Condensation with Gradient Matching

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T14:06:49.095806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36f68ca8-a4e3-4393-b24d-cc35fa1c6ebb · outbound

This paper cites an unresolved cited work.

Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:06:50.128686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c4c832b8-5dfa-46bd-a9df-363fc03b637a · outbound

This paper cites C., Wang, Z., and Lin, D.

Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning C., Wang, Z., and Lin, D

Reference 1998

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:06:51.333588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 23a1a171-89ec-4a1b-a357-f81fe75407be · outbound

This paper cites 2017.2773081.

Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning 2017.2773081

Reference 2018

Resolution: malformed identifier; no resolver link, observed 2026-08-07T14:06:48.270489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 331ce264-b77a-4303-9639-16b802f4e462 · outbound

This paper cites an unresolved cited work.

Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning Unresolved cited work

Reference 2019

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:06:51.136080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 490cd830-ca15-4f00-b325-40189579ada3 · outbound

This paper cites New Insights on Reducing Abrupt Representation Change in Online Continual Learning.

Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning New Insights on Reducing Abrupt Representation Change in Online Continual Learning

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T14:06:48.072204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3f82c5e-e3a2-46e5-90e4-2299251fd7c2 · outbound

This paper cites E., Li, G., Wang, T., and Feng, J.

Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning E., Li, G., Wang, T., and Feng, J

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:06:50.733619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8ac94dba-e5d4-4dfc-9551-25ceb137b7f4 · outbound

This paper cites Optimization of Eqn.

Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning Optimization of Eqn

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:06:50.511331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6752706c-8a48-4f69-a5a4-cdddfb9fc71f · outbound

This paper cites Dual-CBA: Improving Online Continual Learning via Dual Continual Bias Adaptors from a Bi-level Optimization Perspective.

Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning Dual-CBA: Improving Online Continual Learning via Dual Continual Bias Adaptors from a Bi-level Optimization Perspective

Reference 2023

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:06:49.899343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d367977d-2bfd-4f00-bf72-1201464109ae · outbound

This paper cites Dataset Distillation.

Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning Dataset Distillation

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T14:06:48.545314Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.