Paper Citation Record · LEDGER

Color-Oriented Redundancy Reduction in Dataset Distillation

As of 23 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2411.11329.

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

pith.paper-citation-record.v1
2411.11329 v2

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:44:39.010325Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

49 of 49 outbound references displayed

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verified fuzzy33
unresolved16
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ddc5a836-704e-4418-882f-502d30674928 · outbound

This paper cites Slic superpixels compared to state-of-the-art superpixel methods.

Color-Oriented Redundancy Reduction in Dataset Distillation Slic superpixels compared to state-of-the-art superpixel methods

Reference 1

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Observation b8e80e76-f761-48e8-88a6-32f25f6b8390 · outbound

This paper cites Graph cuts in vision and graphics: Theories and applications.

Color-Oriented Redundancy Reduction in Dataset Distillation Graph cuts in vision and graphics: Theories and applications

Reference 2

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Observation 291815e1-2b3d-4114-a2ce-0019403039ce · outbound

This paper cites Dataset distillation by matching training trajectories.

Color-Oriented Redundancy Reduction in Dataset Distillation Dataset distillation by matching training trajectories

Reference 3

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Observation f4f282dc-8118-4d61-b6b8-4c1210584722 · outbound

This paper cites Efros, and Jun-Yan Zhu.

Color-Oriented Redundancy Reduction in Dataset Distillation Efros, and Jun-Yan Zhu

Reference 4

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Observation d2e594ff-71c4-4f2e-a076-66d4069e8945 · outbound

This paper cites Generalizing dataset distillation via deep generative prior.

Color-Oriented Redundancy Reduction in Dataset Distillation Generalizing dataset distillation via deep generative prior

Reference 5

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Observation 0f6eea8e-0c15-4d9a-9f5d-fe83ad4ad3bf · outbound

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

Color-Oriented Redundancy Reduction in Dataset Distillation Scaling up dataset distillation to imagenet- 1k with constant memory

Reference 6

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Observation ac98982a-701b-438e-9f51-42d810664a19 · outbound

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

Color-Oriented Redundancy Reduction in Dataset Distillation Imagenet: A large- scale hierarchical image database

Reference 7

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Observation 80fcd97e-1331-41d4-8bab-b0a7ba290f08 · outbound

This paper cites Peer group filtering and perceptual color image quantization.

Color-Oriented Redundancy Reduction in Dataset Distillation Peer group filtering and perceptual color image quantization

Reference 8

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Observation 4be09174-8925-43ca-82f9-02c64d3c6cef · outbound

This paper cites Remember the past: Distilling datasets into addressable memories for neural networks.

Color-Oriented Redundancy Reduction in Dataset Distillation Remember the past: Distilling datasets into addressable memories for neural networks

Reference 9

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Observation f5c34f58-1d3d-46f5-8282-823439fb06fc · outbound

This paper cites Minimizing the accumulated trajectory error to improve dataset distillation.

Color-Oriented Redundancy Reduction in Dataset Distillation Minimizing the accumulated trajectory error to improve dataset distillation

Reference 10

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Observation b284da1d-800a-47fb-93ba-f20097e3b1a5 · outbound

This paper cites A simple method for color quantization: Octree quantization.

Color-Oriented Redundancy Reduction in Dataset Distillation A simple method for color quantization: Octree quantization

Reference 11

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Observation dc127dc3-a3ef-4e44-8878-37a177893986 · outbound

This paper cites Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching.

Color-Oriented Redundancy Reduction in Dataset Distillation Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching

Reference 12

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Observation 73722e46-eabe-4d1a-b7f7-a4b96b40d8b0 · outbound

This paper cites An adaptive grayscale watermarking method in spatial domain.

Color-Oriented Redundancy Reduction in Dataset Distillation An adaptive grayscale watermarking method in spatial domain

Reference 13

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Observation 27917457-27cd-43ac-8221-51b17a52c899 · outbound

This paper cites Deep residual learning for image recognition.

Color-Oriented Redundancy Reduction in Dataset Distillation Deep residual learning for image recognition

Reference 14

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Observation 6a655e28-bc71-410a-a760-93a7cfbeeb3b · outbound

This paper cites Color image quantization for frame buffer display.

Color-Oriented Redundancy Reduction in Dataset Distillation Color image quantization for frame buffer display

Reference 15

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Observation fd40710f-2c23-472c-b018-35c2286f6355 · outbound

This paper cites Learning to structure an image with few colors.

Color-Oriented Redundancy Reduction in Dataset Distillation Learning to structure an image with few colors

Reference 16

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Observation 8269271b-f677-40b7-a494-7ca8137a4b5a · outbound

This paper cites Learning to structure an image with few colors and beyond, 2022.

Color-Oriented Redundancy Reduction in Dataset Distillation Learning to structure an image with few colors and beyond, 2022

Reference 17

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Observation 972b33de-1dfb-4c4b-9151-8b312058741f · outbound

This paper cites Submodular combina- torial information measures with applications in machine learning.

Color-Oriented Redundancy Reduction in Dataset Distillation Submodular combina- torial information measures with applications in machine learning

Reference 18

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Observation 18b3d059-b75e-4fe5-8678-41f694c5d3f1 · outbound

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

Color-Oriented Redundancy Reduction in Dataset Distillation Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 19

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Observation 34e16b8c-ba72-4beb-9d99-c871a6b51b67 · outbound

This paper cites Dataset condensation via efficient synthetic-data param- eterization.

Color-Oriented Redundancy Reduction in Dataset Distillation Dataset condensation via efficient synthetic-data param- eterization

Reference 20

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Observation 399e65ac-0bf2-4c02-9c50-21558eacce83 · outbound

This paper cites Krizhevsky and G.

Color-Oriented Redundancy Reduction in Dataset Distillation Krizhevsky and G

Reference 21

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Observation dfcb857d-40e1-43c3-8252-f818e553df31 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

Color-Oriented Redundancy Reduction in Dataset Distillation Imagenet classification with deep convolutional neural networks

Reference 22

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Observation c3e98350-6956-4239-ab05-d593616f352f · outbound

This paper cites Dataset Condensation with Latent Space Knowledge Factorization and Sharing.

Color-Oriented Redundancy Reduction in Dataset Distillation Dataset Condensation with Latent Space Knowledge Factorization and Sharing

Reference 23

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Observation 53347307-6ea5-4143-bf09-1c4259141d61 · outbound

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Color-Oriented Redundancy Reduction in Dataset Distillation Dataset distillation via factorization

Reference 24

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Observation c33b18ed-0b6f-4f4b-bbe0-e295a3f6a0bc · outbound

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Color-Oriented Redundancy Reduction in Dataset Distillation Dream: Efficient dataset distillation by representative matching

Reference 25

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Observation 1270e5f7-6d4c-4795-83cf-df93ea270fa1 · outbound

This paper cites Multivariate information transmission.

Color-Oriented Redundancy Reduction in Dataset Distillation Multivariate information transmission

Reference 26

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Observation a5b77e2f-251c-4608-b558-094987600286 · outbound

This paper cites Practical full resolution learned lossless image compression.

Color-Oriented Redundancy Reduction in Dataset Distillation Practical full resolution learned lossless image compression

Reference 27

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Observation d6cde12c-342d-4324-bd60-72c446246d1d · outbound

This paper cites Dataset Meta-Learning from Kernel Ridge-Regression.

Color-Oriented Redundancy Reduction in Dataset Distillation Dataset Meta-Learning from Kernel Ridge-Regression

Reference 28

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Observation 457ac2e5-7486-491f-8e6b-a608eddc631f · outbound

This paper cites Dataset distillation with infinitely wide convolutional networks.

Color-Oriented Redundancy Reduction in Dataset Distillation Dataset distillation with infinitely wide convolutional networks

Reference 29

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Observation bfd566fa-97ad-496d-8d61-14d688bc782f · outbound

This paper cites Dataset distillation with infinitely wide convolutional networks.

Color-Oriented Redundancy Reduction in Dataset Distillation Dataset distillation with infinitely wide convolutional networks

Reference 30

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Observation 60c26f1a-4d77-44d5-b812-7e7d817ed278 · outbound

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Color-Oriented Redundancy Reduction in Dataset Distillation Color quantization of images

Reference 31

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Observation 72598383-b1a8-4714-926f-32d4cc140e1d · outbound

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Color-Oriented Redundancy Reduction in Dataset Distillation Datadam: Efficient dataset distillation with attention matching

Reference 32

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This paper cites Active Learning for Convolutional Neural Networks: A Core-Set Approach.

Color-Oriented Redundancy Reduction in Dataset Distillation Active Learning for Convolutional Neural Networks: A Core-Set Approach

Reference 33

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This paper cites A mathematical theory of communication.

Color-Oriented Redundancy Reduction in Dataset Distillation A mathematical theory of communication

Reference 34

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Observation 570af15d-e4b6-4d41-8895-5c144619c709 · outbound

This paper cites Frequency domain-based dataset distilla- tion.

Color-Oriented Redundancy Reduction in Dataset Distillation Frequency domain-based dataset distilla- tion

Reference 35

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Observation 72284144-7d23-43ca-b840-f6adfd41934a · outbound

This paper cites Pixel recurrent neural networks.

Color-Oriented Redundancy Reduction in Dataset Distillation Pixel recurrent neural networks

Reference 36

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Observation 90793196-e27f-4e5b-ac8c-8ef329d8356b · outbound

This paper cites On implicit bias in overparameterized bilevel optimization.

Color-Oriented Redundancy Reduction in Dataset Distillation On implicit bias in overparameterized bilevel optimization

Reference 37

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Observation 5706af70-ebab-444c-a03f-ceba29890ab3 · outbound

This paper cites DiM: Distilling Dataset into Generative Model.

Color-Oriented Redundancy Reduction in Dataset Distillation DiM: Distilling Dataset into Generative Model

Reference 38

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Observation c6ccc134-4efe-46f7-b49e-805d4efb3332 · outbound

This paper cites Cafe: Learning to condense dataset by aligning features.

Color-Oriented Redundancy Reduction in Dataset Distillation Cafe: Learning to condense dataset by aligning features

Reference 39

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Observation 6f22a70e-8ec3-4777-ab44-4bea5ce713ff · outbound

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Color-Oriented Redundancy Reduction in Dataset Distillation Dataset Distillation

Reference 40

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This paper cites Sparse parameterization for epitomic dataset distillation.

Color-Oriented Redundancy Reduction in Dataset Distillation Sparse parameterization for epitomic dataset distillation

Reference 41

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Observation d24d3a99-ce0b-4d5f-8fad-6d30ecbce2ee · outbound

This paper cites Herding dynamical weights to learn.

Color-Oriented Redundancy Reduction in Dataset Distillation Herding dynamical weights to learn

Reference 42

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Observation 515dee6b-057d-4871-9702-5946f1c3d1e9 · outbound

This paper cites Color quantization by dynamic programming and principal analysis.

Color-Oriented Redundancy Reduction in Dataset Distillation Color quantization by dynamic programming and principal analysis

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:44:39.134478Z

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Observation e3194e5d-b057-4c31-b758-d9d096d23167 · outbound

This paper cites Dataset condensation with differentiable siamese augmentation.

Color-Oriented Redundancy Reduction in Dataset Distillation Dataset condensation with differentiable siamese augmentation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:44:39.126042Z

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Observation 7c3dd69d-dd63-4113-9483-bfcf0453a83c · outbound

This paper cites Synthesizing Informative Training Samples with GAN.

Color-Oriented Redundancy Reduction in Dataset Distillation Synthesizing Informative Training Samples with GAN

Reference 45

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Observation a61779c8-c9c4-4214-9184-de039fb7e870 · outbound

This paper cites Dataset condensation with distribution matching.

Color-Oriented Redundancy Reduction in Dataset Distillation Dataset condensation with distribution matching

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:44:39.117191Z

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Observation 74449f2c-d8c3-4ab3-ad5f-39916f31a89a · outbound

This paper cites Dataset Condensation with Gradient Matching.

Color-Oriented Redundancy Reduction in Dataset Distillation Dataset Condensation with Gradient Matching

Reference 47

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Observation 45efabbc-bd7d-4232-80f6-b9f145ffd4cb · outbound

This paper cites Improved distribution matching for dataset condensation.

Color-Oriented Redundancy Reduction in Dataset Distillation Improved distribution matching for dataset condensation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:44:39.108173Z

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Observation f9d062a7-50ab-4c15-86c3-5d129bb8b096 · outbound

This paper cites Dataset distillation using neural feature regression.

Color-Oriented Redundancy Reduction in Dataset Distillation Dataset distillation using neural feature regression

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:44:39.099187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

No inbound Pith citation observations are available.