Paper Citation Record · LEDGER

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation

As of 18 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2411.19946.

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

pith.paper-citation-record.v1
2411.19946 v2

Coverage vector

measured 48 of 48 reference resolution

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measured 48 of 48 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

48 of 48 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation aa970f21-3c1e-413c-97a4-fa0894d72cb5 · outbound

This paper cites Understand- ing and improving early stopping for learning with noisy la- bels.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Understand- ing and improving early stopping for learning with noisy la- bels

Reference 1

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Observation 73bbe83f-7e0a-44d0-80f7-f4cc33149939 · outbound

This paper cites YOLOv4: Optimal Speed and Accuracy of Object Detection.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation YOLOv4: Optimal Speed and Accuracy of Object Detection

Reference 2

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Observation 3912a814-b727-40a4-b2f1-51c97e9782c7 · outbound

This paper cites Dataset distillation by matching training trajectories.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Dataset distillation by matching training trajectories

Reference 3

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Observation 657eb411-d3c9-4df7-9304-abd479da1c5d · outbound

This paper cites Data distillation can be like vodka: Distilling more times for better quality.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Data distillation can be like vodka: Distilling more times for better quality

Reference 4

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Observation d8bceb74-bd9f-4def-a8d9-f1f8c872dd5c · outbound

This paper cites Randaugment: Practical automated data augmen- tation with a reduced search space.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Randaugment: Practical automated data augmen- tation with a reduced search space

Reference 5

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Observation 83207ae4-4e55-4281-a0f9-0a862bd44c3d · outbound

This paper cites Scaling up dataset distillation to imagenet-1k with constant memory.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Scaling up dataset distillation to imagenet-1k with constant memory

Reference 6

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Observation 68c10755-2387-42a9-916b-56b98506dc67 · outbound

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

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Imagenet: A large-scale hierarchical image database

Reference 7

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Observation c9655ef3-231d-471d-ab77-d228916d80b6 · outbound

This paper cites Diversity-driven synthesis: Enhancing dataset distillation through directed weight adjustment.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Diversity-driven synthesis: Enhancing dataset distillation through directed weight adjustment

Reference 8

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Observation 654b84b2-b138-4e3e-b115-36bf45ec0436 · outbound

This paper cites Fastai/imagenette: A smaller subset of 10 easily clas- sified classes from imagenet, and a little more french.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Fastai/imagenette: A smaller subset of 10 easily clas- sified classes from imagenet, and a little more french

Reference 9

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Observation 6a03bfdb-de59-48d1-904e-73c9d347d158 · outbound

This paper cites Dynamic few-shot visual learning without forgetting.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Dynamic few-shot visual learning without forgetting

Reference 10

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Observation 0fa535a2-6f0d-4623-9a77-737a241d607e · outbound

This paper cites Efficient dataset distillation via minimax diffusion.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Efficient dataset distillation via minimax diffusion

Reference 11

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Observation a722bb80-668c-477a-b786-23b1f8331588 · outbound

This paper cites Towards lossless dataset distillation via difficulty-aligned trajectory matching.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Towards lossless dataset distillation via difficulty-aligned trajectory matching

Reference 12

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Observation 78b55875-634a-4a68-809c-771c24a445b3 · outbound

This paper cites Deep residual learning for image recognition.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Deep residual learning for image recognition

Reference 13

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Observation d677ae88-a90f-4727-bb06-2297b6404356 · outbound

This paper cites Multisize dataset condensation.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Multisize dataset condensation

Reference 14

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Observation b2006a84-d1dc-472e-9d09-f1777bfbf68e · outbound

This paper cites Dataset condensation via efficient synthetic- data parameterization.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Dataset condensation via efficient synthetic- data parameterization

Reference 15

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Observation 10158edc-d394-44f2-b87a-926813073f40 · outbound

This paper cites Learning multiple layers of features from tiny images.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Learning multiple layers of features from tiny images

Reference 16

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Observation be2800c8-0d0a-434d-82dc-4655507bd19a · outbound

This paper cites Tiny imagenet visual recognition challenge.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Tiny imagenet visual recognition challenge

Reference 17

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Observation 4bc482ba-d38e-41bf-a0b1-c14a161e8e02 · outbound

This paper cites Dataset condensation with con- trastive signals.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Dataset condensation with con- trastive signals

Reference 18

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Observation 1541e025-2405-4e0d-9947-ab93dc36e37c · outbound

This paper cites Dataset Distillation via the Wasserstein Metric.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Dataset Distillation via the Wasserstein Metric

Reference 19

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Observation 33afb1d7-9266-45b8-8f8e-857de534eda9 · outbound

This paper cites Investigating bi-level optimization for learn- ing and vision from a unified perspective: A survey and be- yond.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Investigating bi-level optimization for learn- ing and vision from a unified perspective: A survey and be- yond

Reference 20

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Observation e3226c04-2762-4245-963b-f368d081bcd6 · outbound

This paper cites Dataset distillation via factorization.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Dataset distillation via factorization

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:44:25.432446Z

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Observation 3c0c7a93-fc28-4b94-b7b4-cbe0738ecfbf · outbound

This paper cites Learning efficient convolutional networks through network slimming.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Learning efficient convolutional networks through network slimming

Reference 22

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Observation 118490ba-eaf2-4b28-8c1b-92f4676467e0 · outbound

This paper cites Pytorch: An im- perative style, high-performance deep learning library.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Pytorch: An im- perative style, high-performance deep learning library

Reference 23

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Observation 16911afd-970d-404d-b482-66985d4da8e4 · outbound

This paper cites Early stopping-but when? In Neural Net- works: Tricks of the trade, pages 55–69.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Early stopping-but when? In Neural Net- works: Tricks of the trade, pages 55–69

Reference 24

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Observation 8bf5cfd8-65d0-4e11-a383-6b2f3e834d2f · outbound

This paper cites Distributional Dataset Distillation with Subtask Decomposition.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Distributional Dataset Distillation with Subtask Decomposition

Reference 25

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Observation 3d2c61ae-7294-4a64-9fd7-6b74fc22d05a · outbound

This paper cites Designing network design spaces.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Designing network design spaces

Reference 26

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Observation ccb4ebcf-e15b-46e6-bab9-9ef595ccaab1 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 27

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Observation 8796a1c6-685e-4f54-b384-7230666586d4 · outbound

This paper cites Generalized large-scale data condensa- tion via various backbone and statistical matching.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Generalized large-scale data condensa- tion via various backbone and statistical matching

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:44:25.386291Z

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Observation 3d939d33-fbb1-46ce-9341-c22d9ce86fe4 · outbound

This paper cites A fast knowledge distillation framework for visual recognition.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation A fast knowledge distillation framework for visual recognition

Reference 29

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Observation 3c9dbf4e-78e7-4bf8-89b1-841272835937 · outbound

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

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 30

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Observation 70d5c845-dbce-41e5-b119-7aef1edf40ad · outbound

This paper cites Data-free parameter pruning for Deep Neural Networks.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Data-free parameter pruning for Deep Neural Networks

Reference 31

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Observation 3fd82cbf-9728-441f-8ec6-7f0e9dc05626 · outbound

This paper cites On the diversity and realism of distilled dataset: An efficient dataset distilla- tion paradigm.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation On the diversity and realism of distilled dataset: An efficient dataset distilla- tion paradigm

Reference 32

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Observation 759f2c3d-edb2-452c-b010-57f17141e984 · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 33

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Observation 46e48fe9-b998-414b-bd3c-b5d983620907 · outbound

This paper cites Mnas- net: Platform-aware neural architecture search for mobile.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Mnas- net: Platform-aware neural architecture search for mobile

Reference 34

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Observation 65759490-bda8-486e-9696-4500553789e0 · outbound

This paper cites Con- trastive multiview coding.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Con- trastive multiview coding

Reference 35

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DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Dataset Distillation

Reference 36

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Observation 8d6452a9-7fd3-447d-bfe1-a812daa9bacd · outbound

This paper cites Mean squared error: Love it or leave it? a new look at signal fidelity measures.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Mean squared error: Love it or leave it? a new look at signal fidelity measures

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:44:25.338825Z

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Observation 2bbe7bbd-3a23-4e30-81e5-3e661a0c194d · outbound

This paper cites Are large-scale soft labels nec- essary for large-scale dataset distillation? In Advances in neural information processing systems, 2024.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Are large-scale soft labels nec- essary for large-scale dataset distillation? In Advances in neural information processing systems, 2024

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:44:25.330055Z

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Observation 39b9133c-20c4-4551-8be6-dd490ab52246 · outbound

This paper cites Dataset distillation via cur- riculum data synthesis in large data era.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Dataset distillation via cur- riculum data synthesis in large data era

Reference 39

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Observation 30be7e3a-2eff-468c-a9f9-fd06e297c3ab · outbound

This paper cites Squeeze, recover and relabel: Dataset condensation at imagenet scale from a new perspective.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Squeeze, recover and relabel: Dataset condensation at imagenet scale from a new perspective

Reference 40

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Observation 3d315857-4166-4624-b8a8-6412b5f7ab93 · outbound

This paper cites An Introduction to Bi-level Optimization: Foundations and Applications in Signal Processing and Machine Learning.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation An Introduction to Bi-level Optimization: Foundations and Applications in Signal Processing and Machine Learning

Reference 41

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Observation c5dc41cc-b8de-4998-8408-a4cd2d70dfe4 · outbound

This paper cites Dataset condensation with dis- tribution matching.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Dataset condensation with dis- tribution matching

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:44:25.302364Z

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Observation c1f43320-36dd-4de1-a906-16b1b374ac48 · outbound

This paper cites Dataset Condensation with Gradient Matching.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Dataset Condensation with Gradient Matching

Reference 43

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Observation 8baa2740-c68a-4b51-b7cb-30f6a4b51467 · outbound

This paper cites Decoupled knowledge distillation.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Decoupled knowledge distillation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:44:25.292253Z

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Observation 9d5a3c8c-de70-4bca-adf4-8ba8e562d86e · outbound

This paper cites Im- proved distribution matching for dataset condensation.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Im- proved distribution matching for dataset condensation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:44:25.282764Z

Source-reported events for the cited work

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Observation 7fa50bc6-4118-460b-aa83-1746b31c2b73 · outbound

This paper cites Self-supervised Dataset Distillation: A Good Compression Is All You Need.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Self-supervised Dataset Distillation: A Good Compression Is All You Need

Reference 46

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Observation 0bdc9013-31be-42be-912d-eb7dab4f4584 · outbound

This paper cites Dataset distillation using neural feature regression.Advances in Neu- ral Information Processing Systems, 35:9813–9827, 2022.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Dataset distillation using neural feature regression.Advances in Neu- ral Information Processing Systems, 35:9813–9827, 2022

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:44:25.273661Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 38dd8abc-28da-4472-bc3e-997e59a664c2 · outbound

This paper cites Hyper-parameter settings.

DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Hyper-parameter settings

Reference 48

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

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