Paper Citation Record · LEDGER

Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation

As of 10 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2502.01865.

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

pith.paper-citation-record.v1
2502.01865 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:15:39.416806Z

measured 19 of 19 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

19 of 19 outbound references displayed

verified exact0
verified fuzzy4
unresolved14
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ad3dbc7e-a428-4d38-9e7d-2238b7d3b7f3 · outbound

This paper cites an unresolved cited work.

Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-09T14:15:39.700776Z

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 073918db-e43d-4bb2-b256-1c3e67c3a9f0 · outbound

This paper cites Dataset Pruning: Reducing Training Data by Examining Generalization Influence.

Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T14:15:39.371692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49589aea-2fbf-4f1c-b4f9-42262cc92e92 · outbound

This paper cites 0 2000 4000 6000 Training Iteration 0.010 0.015 0.020 0.025 0.030 0.035 0.040 0.045 Learning Rate Dynamic of Learning Rate Learning First Order Second Order Figure.

Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation 0 2000 4000 6000 Training Iteration 0.010 0.015 0.020 0.025 0.030 0.035 0.040 0.045 Learning Rate Dynamic of Learning Rate Learning First Order Second Order Figure

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:15:39.715482Z

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 45c82680-faae-441e-953d-d99825727d0a · outbound

This paper cites an unresolved cited work.

Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-09T14:15:39.685257Z

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 3048d948-9a6c-4755-9ca7-946465a91229 · outbound

This paper cites For a fair comparison, the hyperparameters of each method are properly tuned for the adaption to all the tasks including Cifar100 with 1 IPC and Tiny ImageNet with 3 IPC.

Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation For a fair comparison, the hyperparameters of each method are properly tuned for the adaption to all the tasks including Cifar100 with 1 IPC and Tiny ImageNet with 3 IPC

Reference 13

Resolution: malformed identifier; raw_fallback, observed 2026-08-09T14:15:39.670678Z

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 96e81bf9-5a66-4f08-8100-6d62141b66c3 · outbound

This paper cites an unresolved cited work.

Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-09T14:15:39.655430Z

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 3897babf-6b2c-4adf-be1a-d4f6cec82525 · outbound

This paper cites Cifar10 categorises 50,000 images with the size 32 × 32 into 10 classes while Cifar100 further categorises each of those 10 classes into 10 fine-grained subcategories.

Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation Cifar10 categorises 50,000 images with the size 32 × 32 into 10 classes while Cifar100 further categorises each of those 10 classes into 10 fine-grained subcategories

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:15:39.639278Z

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 88bd9bd6-664b-4fbb-a6f4-53dc94e2f869 · outbound

This paper cites an unresolved cited work.

Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-09T14:15:39.609786Z

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 fcc6afcf-664d-4a80-909c-cd06235e2a60 · outbound

This paper cites an unresolved cited work.

Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-09T14:15:39.595841Z

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 6a8fd474-c37f-468a-a6bd-025204d24f71 · outbound

This paper cites We evaluate our methods on four main image datasets, Cifar10 (Krizhevsky et al., 2009), Cifar100 (Krizhevsky et al., 2009), TinyImageNet (Le & Yang,.

Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation We evaluate our methods on four main image datasets, Cifar10 (Krizhevsky et al., 2009), Cifar100 (Krizhevsky et al., 2009), TinyImageNet (Le & Yang,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:15:39.623671Z

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 6d01bcdd-c133-4715-b33c-3eee267ffa3a · outbound

This paper cites The trained neural networks are evaluated on the real test sets for generalization ability comparison of the synthetic datasets.

Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation The trained neural networks are evaluated on the real test sets for generalization ability comparison of the synthetic datasets

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:15:39.580933Z

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 eb98d921-e446-43aa-8b6c-f226706b3866 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-09T14:15:39.357642Z

Source-reported events for the cited work

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Observation 57290f38-606d-4f78-9cdf-c3b26038eec7 · outbound

This paper cites Gradient Noise Convolution (GNC): Smoothing Loss Function for Distributed Large-Batch SGD.

Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation Gradient Noise Convolution (GNC): Smoothing Loss Function for Distributed Large-Batch SGD

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-09T14:15:39.342590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce270825-4db1-498a-b4e8-d821cf076f8b · outbound

This paper cites Sharpness-Aware Minimization for Efficiently Improving Generalization.

Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation Sharpness-Aware Minimization for Efficiently Improving Generalization

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-09T14:15:39.331786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03a0a71c-2c44-4786-998c-0d1e5b432cfe · outbound

This paper cites SmoothOut: Smoothing Out Sharp Minima to Improve Generalization in Deep Learning.

Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation SmoothOut: Smoothing Out Sharp Minima to Improve Generalization in Deep Learning

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-09T14:15:39.367061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1258c755-5eb4-4c35-9a57-465abd2bd93b · outbound

This paper cites Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions.

Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-09T14:15:39.352564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47836786-996a-4978-91f8-aed2002e0b8c · outbound

This paper cites Watt For What: Rethinking Deep Learning's Energy-Performance Relationship.

Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation Watt For What: Rethinking Deep Learning's Energy-Performance Relationship

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-09T14:15:39.337693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88fb222f-af32-46d0-9e94-bdd1542cadf6 · outbound

This paper cites On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima.

Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-09T14:15:39.347636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 292de2b9-1578-4723-b006-14c1fdd5dc52 · outbound

This paper cites Dataset Distillation.

Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation Dataset Distillation

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-09T14:15:39.362326Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.