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REVIEW 5 major objections 4 minor 49 references

Color-Oriented Redundancy Reduction in Dataset Distillation

T0 review · 5 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Distilled images keep their accuracy when each channel is cut from 256 colors to 64.

desk verdict The color-parameterization idea and empirical gains are real, but the storage-efficiency claim is undercut by an inconsistent accounting and a bit-depth error. read the letter →

arxiv 2411.11329 v2 pith:ZQRS7C4L submitted 2024-11-18 cs.CV

Bowen Yuan , Zijian Wang , Mahsa Baktashmotlagh , Yadan Luo , Zi Huang This is my paper · ORCID
classification cs.CV
keywords datasetdistillationcolorquantizationpalettenetworkstorageefficiencysyntheticdatatrajectorymatchingsubmodularselectionCIFAR
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Dataset distillation compresses a large training set into a small set of synthetic images, but those images are stored in full 8-bit color even though much of that color information is redundant. This paper argues that a distilled image can be represented with far fewer colors—64 per channel instead of 256—without losing the low-level features, such as edges and shapes, that matter for training, and that the freed storage can be spent on more diverse samples. To make that reduction work, it introduces a palette network that learns which pixels deserve which of the limited colors, plus a color-guided initialization that picks starting images with the least overlapping color patterns. If the claim holds, dataset distillation can either use much less memory for the same accuracy or use the saved budget to store more images per class.

What carries the argument

The load-bearing object is the palette network, a small convolutional network that takes a synthetic image and outputs a per-pixel probability distribution over K color buckets for each channel. From these assignments the method builds a per-image palette by averaging the pixel values that fall in each bucket, then reconstructs a color-condensed image by mapping every pixel to its bucket's average color. Three auxiliary losses carry the argument: the maximum color loss pushes every bucket to be used by at least one pixel, the palette balance loss spreads pixel assignments evenly across buckets, and an alignment regularizer keeps the learned clustering close to Median Cut so that the initialization's assumptions hold. On top of that, color-guided initialization uses a generalized graph cut over gradient similarities of quantized images to pick starting images with minimally overlapping color patterns.

What would settle it

Compute the exact on-disk size of an AutoPalette synthetic dataset, including each image's own 64-color palette and the bit-packed pixel indices, and compare it with the 8-bit storage of the same synthetic images; if the total is close to or above 8-bit storage, the claimed 'same storage budget' comparisons are not what they appear.

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Extended reading notes

Core claim

The central claim is that the color space of distilled images is over-provisioned: most of the 256 colors per channel in a synthetic image are not needed for a downstream network to learn the discriminative structure, so the synthetic dataset can be parameterized with as few as 64 colors per channel (a 6-bit palette) and still train models that match or beat models trained on 8-bit synthetic images from other distillation methods. AutoPalette realizes this with a palette network that maps each pixel to one of K color buckets per channel, a per-image palette computed by averaging the pixels assigned to each bucket, and auxiliary losses that force every bucket to be used and used in balance. It further reduces redundancy between images by initializing the synthetic set with real images selected for low color-pattern replication, measured by submodular information gain on Median-Cut-quantized copies. With the same storage budget, the paper reports gains of 1.7% on CIFAR-10 and 4.2% on CIFAR-100 over existing parameterization-based distillation methods.

Load-bearing premise

The claimed storage savings depend on the assumption that a single color palette can be shared by all images in a class, but the method as described computes a separate palette per image, so palette storage is not charged to the budget.

Editorial extensions

If this is right

  • Pixels in distilled images can be stored in 6 bits instead of 8 (64 colors per channel instead of 256), a 25% cut in pixel storage, with accuracy at least matching 8-bit synthetic images.
  • At a fixed storage budget, AutoPalette outperforms other parameterization methods by 1.7% on CIFAR-10 and 4.2% on CIFAR-100, meaning the freed budget can be spent on more images per class.
  • The palette network is framework-agnostic: combining it with trajectory matching or distribution matching improves accuracy in every tested IPC setting, with gains up to 15.7 points on CIFAR-100.
  • Distilled images remain informative at very low color depth: dropping from 256 to 8 colors per channel costs only about 2 points on CIFAR-10 at IPC=10.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper's abstract says the outputs are '4-bit,' but the experiments use 64 colors per channel, which is 6-bit; the pixel-storage saving is about 25% relative to 8-bit, not the 50% a 4-bit format would give. (Inference from the experimental setting.)
  • The storage analysis in Section 3.6 budgets one palette per class, while Eq. (4) builds a different palette per image; if those per-image palettes are stored, the reported memory savings shrink or disappear. (The paper does not address this accounting.)
  • The same palette-network idea could be combined with spatial downsampling or frequency-domain parameterization to compress distilled datasets along multiple axes at once; the paper only varies color depth.
  • A natural follow-up is a shared class-level palette that makes the storage budget and the initialization approximation match the training-time color mapping; the paper does not evaluate that variant.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 4 minor

Summary. The paper proposes AutoPalette, a dataset-distillation framework that reduces color redundancy at two levels: a palette network maps each pixel of a synthetic image to one of K colors per channel (Eqs. 3–5), and a color-guided initialization selects diverse real images using conditional gain of a graph-cut function on median-cut quantized images. The synthetic images are stored in a reduced color space, and the paper claims in the abstract and introduction that models trained on '4-bit' images achieve competitive accuracy and that under the same storage budget AutoPalette outperforms prior methods by 1.7% and 4.2% on CIFAR-10 and CIFAR-100. Experiments are reported on CIFAR-10/100, ImageNet subsets, cross-architecture settings, and ablations.

Significance. The motivation—removing color redundancy in distilled images—is reasonable, and the paper contains a broad set of experiments, including compatibility with DM and TM objectives and cross-architecture evaluation, which are useful. If the storage-efficiency claim were accurately quantified, this could be a meaningful contribution to parameterization-based dataset distillation. However, the central claim is not currently supported: the storage analysis in §3.6 is inconsistent with the per-image palettes defined in §3.3, the '4-bit' characterization is factually incorrect for K=64 colors per channel, and the experimental comparisons do not establish equal storage budgets. These issues concern the headline contribution, so the paper's main significance is not established.

major comments (5)
  1. [§3.3, Eqs. (4)–(5), and §3.6] Equations (4)–(5) define a per-image color palette: for each image, m̃ is the average of that image's pixels assigned to each color bucket, and the reconstructed image b is generated from this image-specific palette. Section 3.6, however, performs the storage analysis with a class-level color budget (Σ_i N_i ≤ 256 and at most 2^{8−n} images per class for n-bit color). The experiments use K=64 colors per channel and IPC=10 or 50; with n=6 bits, the class-level condition permits only 4 images, so the reported configuration violates the analysis, and if per-image palettes are stored, their cost is omitted from the claimed savings. Concretely, for CIFAR (32×32×3, K=64), an index map costs 32·32·3·6 = 18,432 bits and the per-image palette costs 64·3·8 = 1,536 bits, for 19,968 bits per image versus 24,576 bits for 8-bit storage, a saving of roughly 19% rather than the factor implied by the paper. This accounting directly underlies the abstract's 'same storage budget' claim.
  2. [Abstract, §1, and Table 1] The paper states in the abstract and introduction that the framework produces '4-bit images' and that this is the source of storage savings. With K=64 colors per channel, each pixel index requires log2(64)=6 bits per channel, so the images are 6-bit per channel, not 4-bit. Table 1 itself states that the synthetic images are reduced from 256 to 64 colors. The repeated '4-bit' label is incorrect and cannot support the claimed storage comparison.
  3. [§3.3 and §3.5] The color transformation defined by Eqs. (4)–(5) is non-differentiable because it uses the Kronecker delta of the arg max and the arg-max selection h. The overall objective in Eq. (14) differentiates L_task with respect to S and θ_c through B=ϕ_palette(S;θ_c), and Eq. (13) updates the palette network with L_palette, which includes L_task and L_a (Eq. (12), also based on arg max). The manuscript does not describe any straight-through estimator, Gumbel-Softmax, or other gradient approximation for these discrete operations. The training procedure is therefore underspecified at a load-bearing point.
  4. [§4.3 and Tables 1, 3, 7] The text in §4.3 says the ablation fixes the number of synthetic images to 10 per class 'rather than fully utilizing the available storage capacity,' which strongly suggests that the main experiments in Table 1 store more than IPC images per class within the same budget. If that is the case, Tables 1–2 are not comparisons at fixed IPC or fixed storage budget, and the headline improvements are not attributable to color reduction alone. This reading is supported by internal inconsistencies: the full AutoPalette model on CIFAR-10 IPC=10 is 74.3% in Table 1 but 66.20% in Table 3, and Table 7 reports 66.8% for 256 colors with IPC=10. The paper must state exactly how many images and how many bits per class are used in each reported result.
  5. [Eq. (8) and Eq. (11)] Equation (8) defines conditional gain as G(A|C)=G(T^Q)−G(C), which does not depend on the candidate set A and is not the conditional gain used in Appendix A.1 (where f(A|B)=f(A∪B)−f(B)). Equation (11) writes the selection objective as arg max_c G*(A) − 2Σ_{i∈A}Σ_{c∈C} Sim(i,c), but the summation and the arg max use the same symbol c, and the expression does not isolate the candidate being scored, so the selection rule is ambiguous as written. This affects the color-guided initialization, one of the two claimed contributions.
minor comments (4)
  1. [Table 2 caption and §4.2] The caption of Table 2 says 'All experiments are conducted on CIFAR10 with IPC=10 storage budget for parameterization methods,' but the tabulated datasets are ImageNet subsets (ImageNette, ImageWoof, etc.); the caption should be corrected.
  2. [References] References [4] and [5] are the same paper (Cazenavette et al., 'Generalizing dataset distillation via deep generative prior'), and references [28] and [30] also appear to describe the same paper; duplicated references should be consolidated.
  3. [Appendix A.1] In Appendix A.1, the notation f(A|B) is described as the gain of adding set B to set A, but Eq. (16) is written as f(A∪B)−f(B); the notation should be aligned with the main text and with the standard definition of conditional gain.
  4. [§3.4] The symbol C is used both for the unselected set in Eq. (11) and for the number of image channels in §3.3; this overloaded notation makes the initialization equations harder to follow and should be changed.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: AutoPalette's reported accuracies are empirical evaluations driven by the external trajectory-matching objective, not quantities constructed from fitted inputs or self-citations.

full rationale

The paper's central derivation is an empirical optimization loop: synthetic images and the palette network are optimized against the trajectory-matching objective L_task from prior work [3], with auxiliary heuristic regularizers (Lm, Lb, La) that encourage color utilization and balance. The reported test accuracies are independent evaluations on held-out data; no reported number is produced by fitting a parameter to that same number and then renaming it a prediction. The initialization strategy uses traditional Median Cut quantization and a generalized graph-cut selection objective, both external to the final accuracy claim. There are no load-bearing self-citations, no imported uniqueness theorem, and no ansatz smuggled in solely through citation. The paper does contain a serious storage-accounting inconsistency (per-image palettes from Eqs. 4-5 are omitted from the class-level budget in Sec. 3.6) and an arithmetic labeling error (64 colors requires 6 bits, not 4), but these are correctness and experimental-validity flaws, not circularity: the claimed improvements could in principle fail and are not logically forced by the method's definitions. Accordingly, the circularity score is 0.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The method does not introduce new physical entities or forces. The free parameters are design choices for the color depth and loss weighting. The key assumptions are the graph-cut correctness, the trajectory-matching surrogate, and the Median Cut proxy for the palette network.

free parameters (2)
  • Number of colors per channel K = 64
    Chosen as the main operating point; the paper shows results for other values in Table 7 but uses 64 for the headline comparisons.
  • Loss coefficients alpha, beta, gamma = alpha=1, beta=1, gamma=3
    Set without reported sensitivity analysis; may be tuned to the benchmark datasets.
assumptions (3)
  • standard math Generalized graph cut is a monotone submodular function and the conditional gain in Eq. (11) correctly selects diverse samples.
    Invoked in Section 3.4 and proven in Appendix A.1; the proof contains a set-union typo but the final formula is plausible.
  • domain assumption Trajectory matching is a valid surrogate for the dataset distillation objective in Eq. (1).
    Adopted from prior work [3,12] and used as the base distillation framework throughout the experiments.
  • ad hoc to paper Median Cut color quantization on real images approximates the output of the learned palette network.
    Used in the initialization module and regularized by La in Eq. (12); the proxy is not validated against the actual palette network outputs.

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Cite this review

Pith. "Pith review of Color-Oriented Redundancy Reduction in Dataset Distillation." pith.science (2026). https://pith.science/paper/ZQRS7C4L

@misc{pith2026241111329,
  author       = {Pith},
  title        = {Pith review of: Color-Oriented Redundancy Reduction in Dataset Distillation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZQRS7C4L}},
  note         = {Machine review of arXiv:2411.11329}
}
read the original abstract

Dataset Distillation (DD) is designed to generate condensed representations of extensive image datasets, enhancing training efficiency. Despite recent advances, there remains considerable potential for improvement, particularly in addressing the notable redundancy within the color space of distilled images. In this paper, we propose AutoPalette, a framework that minimizes color redundancy at the individual image and overall dataset levels, respectively. At the image level, we employ a palette network, a specialized neural network, to dynamically allocate colors from a reduced color space to each pixel. The palette network identifies essential areas in synthetic images for model training and consequently assigns more unique colors to them. At the dataset level, we develop a color-guided initialization strategy to minimize redundancy among images. Representative images with the least replicated color patterns are selected based on the information gain. A comprehensive performance study involving various datasets and evaluation scenarios is conducted, demonstrating the superior performance of our proposed color-aware DD compared to existing DD methods. The code is available at \url{https://github.com/KeViNYuAn0314/AutoPalette}.

Figures

Figures reproduced from arXiv: 2411.11329 by the authors.

Figure 1
Figure 1. The overview of the proposed AutoPalette framework. Initialization: We compare the [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The visualization of (a) images under 8, 6, 3, 1-bit color depths (b-c) color condensed [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Comparison between the per￾formance of submodular color diversity initialization and random real images ini￾tialization. computing information gain over color-reduced images Effectiveness of Color-guided Initialization under Dif￾ferent Color Depth. Our experiments compare the perfor￾mance of two ways to initialize the base images: one em￾ploying our method of color-guided initialization (denotes GraphCut), and rando… view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: CIFAR10 color condensed synthetic images with ZCA whitening. [PITH_FULL_IMAGE:figures/full_fig_p016_4.png]
Figure 5
Figure 5. Figure 5: CIFAR10 color condensed synthetic images without ZCA whitening. [PITH_FULL_IMAGE:figures/full_fig_p016_5.png]
Figure 6
Figure 6. Figure 6: CIFAR10 synthetic images in 3-bit color depth [PITH_FULL_IMAGE:figures/full_fig_p016_6.png]
Figure 7
Figure 7. Figure 7: Color condensed synthetic images for ImageNette [PITH_FULL_IMAGE:figures/full_fig_p017_7.png]
Figure 8
Figure 8. Figure 8: Color condensed synthetic images for ImageWoof [PITH_FULL_IMAGE:figures/full_fig_p017_8.png]
Figure 9
Figure 9. Figure 9: Color condensed synthetic images for ImageFruit [PITH_FULL_IMAGE:figures/full_fig_p017_9.png]
Figure 10
Figure 10. Figure 10: Color condensed synthetic images for ImageMeow [PITH_FULL_IMAGE:figures/full_fig_p018_10.png]
Figure 11
Figure 11. Figure 11: Color condensed synthetic images for ImageSquawk [PITH_FULL_IMAGE:figures/full_fig_p018_11.png]
Figure 12
Figure 12. Figure 12: Color condensed synthetic images for ImageYellow [PITH_FULL_IMAGE:figures/full_fig_p018_12.png]

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Reference graph

Works this paper leans on

49 extracted references · 33 canonical work pages

  1. [1]

    Slic superpixels compared to state-of-the-art superpixel methods

    Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk. Slic superpixels compared to state-of-the-art superpixel methods. IEEE transactions on pattern analysis and machine intelligence , 34(11):2274–2282, 2012

    2012

  2. [2]

    Graph cuts in vision and graphics: Theories and applications

    Yuri Boykov and Olga Veksler. Graph cuts in vision and graphics: Theories and applications. In Handbook of mathematical models in computer vision , pages 79–96. Springer, 2006

    work page 2006

  3. [3]

    Dataset distillation by matching training trajectories

    George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, and Jun-Yan Zhu. Dataset distillation by matching training trajectories. In Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition , pages 4750–4759, 2022

    2022

  4. [4]

    Efros, and Jun-Yan Zhu

    George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, and Jun-Yan Zhu. Generalizing dataset distillation via deep generative prior. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 3739–3748, 2023

    work page 2023

  5. [5]

    Generalizing dataset distillation via deep generative prior

    George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, and Jun-Yan Zhu. Generalizing dataset distillation via deep generative prior. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3739–3748, 2023

    work page 2023

  6. [6]

    Scaling up dataset distillation to imagenet- 1k with constant memory

    Justin Cui, Ruochen Wang, Si Si, and Cho-Jui Hsieh. Scaling up dataset distillation to imagenet- 1k with constant memory. In International Conference on Machine Learning , pages 6565–6590. PMLR, 2023

    work page 2023

  7. [7]

    Imagenet: A large- scale hierarchical image database

    Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large- scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee, 2009

    2009

  8. [8]

    Peer group filtering and perceptual color image quantization

    Yining Deng, Charles Kenney, Michael S Moore, and BS Manjunath. Peer group filtering and perceptual color image quantization. In 1999 IEEE International Symposium on Circuits and Systems (ISCAS), volume 4, pages 21–24. IEEE, 1999

    work page 1999

Show all 49 references
  1. [9]

    Remember the past: Distilling datasets into addressable memories for neural networks

    Zhiwei Deng and Olga Russakovsky. Remember the past: Distilling datasets into addressable memories for neural networks. Advances in Neural Information Processing Systems , 35:34391– 34404, 2022

    2022

  2. [10]

    Minimizing the accumulated trajectory error to improve dataset distillation

    Jiawei Du, Yidi Jiang, Vincent YF Tan, Joey Tianyi Zhou, and Haizhou Li. Minimizing the accumulated trajectory error to improve dataset distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3749–3758, 2023

    2023

  3. [11]

    A simple method for color quantization: Octree quantization

    Michael Gervautz and Werner Purgathofer. A simple method for color quantization: Octree quantization. In New Trends in Computer Graphics: Proceedings of CG International’88 , pages 219–231. Springer, 1988

    1988

  4. [12]

    To- wards lossless dataset distillation via difficulty-aligned trajectory matching

    Ziyao Guo, Kai Wang, George Cazenavette, Hui Li, Kaipeng Zhang, and Yang You. To- wards lossless dataset distillation via difficulty-aligned trajectory matching. arXiv preprint arXiv:2310.05773, 2023

    2023 arXiv

  5. [13]

    An adaptive grayscale watermarking method in spatial domain

    Jialing Han. An adaptive grayscale watermarking method in spatial domain. Journal of Information and Computational Science , 12:4759–4769, 08 2015

    2015

  6. [14]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 770–778, 2016

    2016

  7. [15]

    Color image quantization for frame buffer display

    Paul Heckbert. Color image quantization for frame buffer display. ACM Siggraph Computer Graphics, 16(3):297–307, 1982

    1982

  8. [16]

    Learning to structure an image with few colors

    Yunzhong Hou, Liang Zheng, and Stephen Gould. Learning to structure an image with few colors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10116–10125, 2020

    2020

  9. [17]

    Learning to structure an image with few colors and beyond, 2022

    Yunzhong Hou, Liang Zheng, and Stephen Gould. Learning to structure an image with few colors and beyond, 2022

    2022

  10. [18]

    Submodular combina- torial information measures with applications in machine learning

    Rishabh Iyer, Ninad Khargoankar, Jeff Bilmes, and Himanshu Asanani. Submodular combina- torial information measures with applications in machine learning. In Algorithmic Learning Theory, pages 722–754. PMLR, 2021. 10

    2021

  11. [19]

    Very deep convolutional networks for large-scale image recognition

    Simonyan Karen. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv: 1409.1556, 2014

    2014 arXiv

  12. [20]

    Dataset condensation via efficient synthetic-data param- eterization

    Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun, Hwanjun Song, Joonhyun Jeong, Jung-Woo Ha, and Hyun Oh Song. Dataset condensation via efficient synthetic-data param- eterization. In International Conference on Machine Learning , pages 11102–11118. PMLR, 2022

    2022

  13. [21]

    Krizhevsky and G

    A. Krizhevsky and G. Hinton. Learning multiple layers of features from tiny images. Master’s thesis, Department of Computer Science, University of Toronto , 2009

    2009

  14. [22]

    Imagenet classification with deep convolutional neural networks

    Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems , 25, 2012

    2012

  15. [23]

    Dataset condensation with latent space knowledge factorization and sharing

    Hae Beom Lee, Dong Bok Lee, and Sung Ju Hwang. Dataset condensation with latent space knowledge factorization and sharing. arXiv preprint arXiv:2208.10494, 2022

    2022 arXiv

  16. [24]

    Dataset distillation via factorization

    Songhua Liu, Kai Wang, Xingyi Yang, Jingwen Ye, and Xinchao Wang. Dataset distillation via factorization. Advances in neural information processing systems , 35:1100–1113, 2022

    2022

  17. [25]

    Dream: Efficient dataset distillation by representative matching

    Yanqing Liu, Jianyang Gu, Kai Wang, Zheng Zhu, Wei Jiang, and Yang You. Dream: Efficient dataset distillation by representative matching. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 17314–17324, 2023

    2023

  18. [26]

    Multivariate information transmission

    William McGill. Multivariate information transmission. Transactions of the IRE Professional Group on Information Theory, 4(4):93–111, 1954

    1954

  19. [27]

    Practical full resolution learned lossless image compression

    Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, and Luc Van Gool. Practical full resolution learned lossless image compression. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 10629–10638, 2019

    2019

  20. [28]

    Dataset meta-learning from kernel ridge-regression

    Timothy Nguyen, Zhourong Chen, and Jaehoon Lee. Dataset meta-learning from kernel ridge-regression. arXiv preprint arXiv:2011.00050, 2020

    2011 arXiv

  21. [29]

    Dataset distillation with infinitely wide convolutional networks

    Timothy Nguyen, Roman Novak, Lechao Xiao, and Jaehoon Lee. Dataset distillation with infinitely wide convolutional networks. In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), pages 5186–5198, 2021

    2021

  22. [30]

    Dataset distillation with infinitely wide convolutional networks

    Timothy Nguyen, Roman Novak, Lechao Xiao, and Jaehoon Lee. Dataset distillation with infinitely wide convolutional networks. Advances in Neural Information Processing Systems , 34:5186–5198, 2021

    2021

  23. [31]

    Color quantization of images

    Michael T Orchard, Charles A Bouman, et al. Color quantization of images. IEEE transactions on signal processing, 39(12):2677–2690, 1991

    1991

  24. [32]

    Datadam: Efficient dataset distillation with attention matching

    Ahmad Sajedi, Samir Khaki, Ehsan Amjadian, Lucy Z Liu, Yuri A Lawryshyn, and Konstanti- nos N Plataniotis. Datadam: Efficient dataset distillation with attention matching. InProceedings of the IEEE/CVF International Conference on Computer Vision , pages 17097–17107, 2023

    2023

  25. [33]

    Active learning for convolutional neural networks: A core-set approach

    Ozan Sener and Silvio Savarese. Active learning for convolutional neural networks: A core-set approach. arXiv preprint arXiv:1708.00489, 2017

    2017 arXiv

  26. [34]

    A mathematical theory of communication

    Claude Elwood Shannon. A mathematical theory of communication. ACM SIGMOBILE mobile computing and communications review, 5(1):3–55, 2001

    2001

  27. [35]

    Frequency domain-based dataset distilla- tion

    Donghyeok Shin, Seungjae Shin, and Il-Chul Moon. Frequency domain-based dataset distilla- tion. Advances in Neural Information Processing Systems , 36, 2024

    2024

  28. [36]

    Pixel recurrent neural networks

    Aäron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. Pixel recurrent neural networks. In International conference on machine learning , pages 1747–1756. PMLR, 2016

    2016

  29. [37]

    On implicit bias in overparameterized bilevel optimization

    Paul Vicol, Jonathan P Lorraine, Fabian Pedregosa, David Duvenaud, and Roger B Grosse. On implicit bias in overparameterized bilevel optimization. In International Conference on Machine Learning, pages 22234–22259. PMLR, 2022

    2022

  30. [38]

    Dim: Distilling dataset into generative model

    Kai Wang, Jianyang Gu, Daquan Zhou, Zheng Zhu, Wei Jiang, and Yang You. Dim: Distilling dataset into generative model. arXiv preprint arXiv:2303.04707, 2023

    2023 arXiv

  31. [39]

    Cafe: Learning to condense dataset by aligning features

    Kai Wang, Bo Zhao, Xiangyu Peng, Zheng Zhu, Shuo Yang, Shuo Wang, Guan Huang, Hakan Bilen, Xinchao Wang, and Yang You. Cafe: Learning to condense dataset by aligning features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 12196–12...

    2022

  32. [40]

    Dataset distillation

    Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros. Dataset distillation. arXiv preprint arXiv:1811.10959, 2018

    2018 arXiv

  33. [41]

    Sparse parameterization for epitomic dataset distillation

    Xing Wei, Anjia Cao, Funing Yang, and Zhiheng Ma. Sparse parameterization for epitomic dataset distillation. Advances in Neural Information Processing Systems , 36, 2024

    2024

  34. [42]

    Herding dynamical weights to learn

    Max Welling. Herding dynamical weights to learn. In Proceedings of the 26th annual interna- tional conference on machine learning , pages 1121–1128, 2009

    2009

  35. [43]

    Color quantization by dynamic programming and principal analysis

    Xiaolin Wu. Color quantization by dynamic programming and principal analysis. ACM Transactions on Graphics (TOG), 11(4):348–372, 1992

    1992

  36. [44]

    Dataset condensation with differentiable siamese augmentation

    Bo Zhao and Hakan Bilen. Dataset condensation with differentiable siamese augmentation. In International Conference on Machine Learning , pages 12674–12685. PMLR, 2021

    2021

  37. [45]

    Synthesizing informative training samples with gan

    Bo Zhao and Hakan Bilen. Synthesizing informative training samples with gan. arXiv preprint arXiv:2204.07513, 2022

    2022 arXiv

  38. [46]

    Dataset condensation with distribution matching

    Bo Zhao and Hakan Bilen. Dataset condensation with distribution matching. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages 6514–6523, 2023

    2023

  39. [47]

    Dataset condensation with gradient matching

    Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen. Dataset condensation with gradient matching. arXiv preprint arXiv:2006.05929, 2020

    2006 arXiv

  40. [48]

    Improved distribution matching for dataset condensation

    Ganlong Zhao, Guanbin Li, Yipeng Qin, and Yizhou Yu. Improved distribution matching for dataset condensation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7856–7865, 2023

    2023

  41. [49]

    Dataset distillation using neural feature regression

    Yongchao Zhou, Ehsan Nezhadarya, and Jimmy Ba. Dataset distillation using neural feature regression. Advances in Neural Information Processing Systems , 35:9813–9827, 2022. 12 A Appendix A.1 Proof for Conditional Information Gain of Graph Cut The generalized graph cut set func...

    2022

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.