Dataset Condensation with Color Compensation
Authors: Huyu Wu
Duo Su
Junjie Hou
Guang Li
Venue: Accepted by TMLR ()

Abstract: Dataset condensation always faces a constitutive trade-off: balancing performance and fidelity under extreme compression. Existing methods struggle with two bottlenecks: image-level selection methods (Coreset Selection, Dataset Quantization) suffer from inefficiency in condensation, while pixel-level optimization (Dataset Distillation) introduces semantic distortion due to over-parameterization. With empirical observations, we find that a critical problem in dataset condensation is the oversight of color's dual role as an information carrier and a basic semantic representation unit. We argue that improving the colorfulness of condensed images is beneficial for representation learning. Motivated by this, we propose DC3: a Dataset Condensation framework with Color Compensation. After a calibrated selection strategy, DC3 utilizes the latent diffusion model to enhance the color diversity of an image rather than creating a brand-new one. Extensive experiments demonstrate the superior performance and generalization of DC3 that outperforms SOTA methods across multiple benchmarks. To the best of our knowledge, besides focusing on downstream tasks, DC3 is the first research to fine-tune pre-trained diffusion models with condensed datasets. The Frechet Inception Distance (FID) and Inception Score (IS) results prove that training networks with our high-quality datasets is feasible without model collapse or other degradation issues.

Published in Transactions on Machine Learning Research (10/2025)
Dataset Condensation with Color Compensation
Huyu Wu
Duo Suz*
Junjie Hou Guang Li
University of Chinese Academy of Sciences TTsinghua University
Hong Kong University of Science and Technology 4 "Hokkaido University
"Corresponding Authors: suduodmail. ts tsinguua edu. cn, guangølmd ist. hww.hoh.aaac..... ac.
// Reviewed on OpenReview: https: // openreveewwww.e formm?? hd=hIdwvI
Abstract
Dataset condensation always faces a constitutive trade-off: balancing performance and fi- delity under extreme compression. Existing methods struggle with two bottlenecks: image- level selection methods (Coresct Sclection, Dataset Quantization) suffer from inefficiency in condensation, while pixel-level oplimization (Dataset Distillation) introduces seman- find that infor-
tic distortion due to wve--parameterizationn. With empirical observations, critical problem in dataset condensation is the oversight of color's dual role mation carrier and a basic semantic representation unit. We argue that improving the colorfulness condensed images is beneficial for representation learning. Motivated by this, we propose DC3: a Dataset Condensation framework with Color Compensation. After a calibrated selection stratogy, DC3 utilizes the latent diffusion model enhance the color diversity of an image rather than creating brand-new one. Extensive experi- ments demonstrate the superior performance and generalization of DC3 that outperforms SOTA methods across multiple benchmarks. To the best of our knowledge, besides focus- ing on downstream tasks, DC3 is the first research to fine-tune pre-trained diffusion mod- els with condensed datasets. The Frechet Inception Distance (FID) and Inception Score (IS) results prove that training networks with our high-quality datascts is feasible with- out model collapse or other degradation issues. Code and generated data are available al http://ttttttoooooooc//ceeeeeett Cancchhoolo--eee Colp:taoatooooo--ooeetttaiiioi
1 Introduction
In data-centric artificial intelligence, the datasct serves as a vehicle of knowledge whose information density and represen- tation capacity determine the cognitive boundaries of model. Information theory reveals fundamental contradiction: There exists coexistence of sparse task-relevant information and re- dundant task-irrelevant noise within raw data (Shannon 1948
Pixel-Level Optimization
Image- Lape-LLA Level Selection
Dataset
Condensation
DC3
Cover 1999: MacKay (003).. Inspired by this contradiction, re- searchers propose dataset condensation, which focuses on creat- ing a small dataset from the original large-scale data to reduce training costs while preserving comparable performance (Geng et al. 2023 Lei & Tao 2023a Yu el. al. 2023a The condensed dataset not only saves storage space but also accelerates 1/O speed.
Dataset Distillation
Dataset Quantization
More compressed
More generalized
Figure Dataset Condensation Tri- angle. Coreset Selection and Dataset Quantization focus on imago-level selection, while Dataset Distillation optimizes pix- els for better condensation. An ideal DC method should balance information com- pression and semantic preservation.
Dataset Condensation (DC) includes Coreset Selection (CS) (Guo et al.) Chai et al.] 2023).
2022 Sinha et al. 2020 Rosman et al. 2014
Datasct Quantization (DQ) (Zhou et
2023 Zhao et al.
2024). and Dataset Distillation (DD) (Liu
al.
2022 Du et al. 2023a Zheng et al. 2024 As illustrated

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in fig. 1 elucidate different DC methods as DC Triangle. The primary distinction of these approaches is that CS and DQ focus on image-level selection, thereby exhibiting better cross-architecture generalization. DD implements pixel-level optimization that results in pronounced condensation performance (Cazenavette 20220. Despite reducing datasct scale and enhancing computational efficiency, the synthetic image ct al.
generated by DD lacks sulficient and consistent semantic information (Su et al. 2024). The compression capability of the matching-basse distillation strategy achieved at the expense of semantic realness. The optimal syuthetic image should neither be too realistic nor too expressive (Cazenavette al., 2023 Du al,, 2023b). Motivated by this observation, seek to propose method that not only ensures condensation capability like DD but also guarantees the lossless preservation of semantic information like CS and DQ.
The essence of datasel condensation lies in exploring how the models extract information from a dataset and what the models actually "see"? Color plays a crucial role in the first principle of human visual percep- tion, forming the basis of how we understand the world (Jameson color has dual identity in machine visual compression: is both the physical signal that needs optimiz- ing (Prativadiibhayankarne
al.) 2020: Boynton 1990p. Similarly,
al. 2023) and the basic unit that maintains the semantic relevance of an image (Van Leeuwen 2011p. Such a dual nature makes color main "battlefield" in dataset condensation, which previous methods have overlooked.
the RGB channels of the distilled datasets follow different distributions com compared to the original datasets (especially for the matching surrogate objective methods (Zhao & Bilen 2023 202 la
As depicted in figs. 2a to 2c
Sajedi et al. 2023). Their Kernel Density Estimation (KDE) curves all tend to have a uniform distribution. Such Color Homogenization phenomenon could hinder the model in understanding the data representa- tions, thus degrading its generalization. Unlike DD, quantized datasets are derived through quantization process from the original data, notably preserving precise color distribution and semantic integrity, whereas lacking the information compression capability inherent to DD. Motivated by these observations, we seek to study: " Can we avoid Color Homogenization, but attain an informative dataset by directly using the image-level selection
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Figure 2: The KDE curves of the normalized RGB pixel value across different DC methods. Previous methods exhibit a Color Homogenization phenomenon in contrast to DC3.
Recent advancements in generative modeling have established diffusion models as a dominant approach for text-lo-image and image-to-image generation. Building upon these successes, pioneering works have adaptod diffusion models to datasct condensation, leveraging their high-fidelity generation capability preserve essential characteristies within compressed datasets (Su et al. 2024 Cazenavetle et al., 2023 Gu et al. 2024 Sajedi et al. 2024). However, diffusion models exhibit limited robustness under sparse ambignous textual conditions, generating images with semantic inconsistency or hallucination that deviate from roal-world distributions.
In this paper, we propose a Dataset Condensation framework with Color Compensation (DC3) to enhance the quality of condensed datasets. DC3 adds color-rclated hues to images via a pre-trained diffusion model. As depicted in fig. [d] the KDE curve is closer to the real dataset than other methods, which conducive enhancing the representation capability. Next, through analyzing the relationship between submodular gain
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and distribution of samples, DC3 reuses the submodular gain to select samples, while the bin generation stage is handed over to K-Means clustering. Consequently, the whole image-level selection process has become more controllable and efficient. DC3 is also a training-free method, whose processing speed depends on the sampler and diffusion networks. The pivotal contributions are summarized as follows:
According to the dataset condensation triangle, we propose DC3 that utilizes the compression ability of dataset distillation along with the generalization ability of dataset quantization. It is universal method that is adaptive to datasets of various scales and resolutions.
Due to Color Homogenization inherent in pixel-level optimization methods, we employ the clustering- based quantization method to get rid this issue and propose the diffusion- based Color Compensation method to enhance the information diversity of condensed images.
In addition the classification task, DC3 also dives into improving the fine-tuning performance of large vision models (LVMs). The FIDs on the fine-tuned stable diffussin and DiT demonstrate that the information is compressed and preserved by DC3.
The experimental results, especially on the hard-to-classin datasets, demonstrate that DC3 achieves the superior performance of dataset condensation and enhances image colorfulness.
2
Related Work
2.1
Dataset Distillation
Datasel Distillation (DD) (Wang et al. 2018 Li et al. 2022 Lei & Tao 2023b Yu al. 2023bb met hods are categorized into matching-basse based and generation-bart based approaches. Matching- based methods can also be categorized into three paradigms: performance matching, parameter matching, and distribution matching. Performance matching focuses on optimizing the synthetic dataset to ensure that models trained on it perform similarly to those trained on the original dataset (Nguyen et al. 2021a Parameter matching, which includes single-step (e.g., gradient matching (Zhao & Bilen 20211 a) and multi-step (e.g., trajectory matching (Cazenavette et al., 2022 Guo et al.) (0244] approaches, aims to align the model parameters or their learning trajectories during training
synthetic and real data. Distribution matching emphasizes narrowing the distribution gap by ensuring that synthetic data approximates the feature distribution of the
original data (Zhao & Bilen 2023 Deng et al. 2024).
Generation based methods have recently gained traction by leveraging modern generative models to by- pass the limitations of iterative sample-wise optimization (Zhao & Bilen 2022 Cazenavette ot al. 2023 D'M (Su et al., 2024) uses latent diffussin models to synthesize data. maintains consistency between real and synthetic image spaces and incorporates label prototypes. Minimax (Gu et al. 2024) embeds a minimax criterion in the diffusion training process. It balances representativenoss and diversity while reducing com- putational costs. IGD Chen et al. 2025) redefines DD as controlled diffusion generation task. It uses trajectory influence function to guide the diffusion process and generate effective synthetic data for training.
2.2 Dataset Quantization
Traditional DD methods often rely on specific network architectures, resulting degraded performance when the distilled datasets are transferred to other architectures (Wang et al. 2018 Zhao & Bilen 2021b;
Cazenavette et al., 2022). To address this limitation, Dataset Quantization (DQ) [hou et al. 2023 in-
troduces a novel framework capable of compressing large-scale datasets and generating compact datasets suitable for training any neural network architecture without sacrificing performance. DQ achieves this by recursively partitioning the dataset into non-overlapping data bins and optimizing sample selection based on submodular gains to maximize dataset diversity, This approach not only enhances dataset diversity but also ensures the broad applicability of the generated subsets across varying network architectures. In a parallel effort to enhance both the diversity and realism of distilled data, Sun al. (2024) introduces RDED, an efficient optimization-free paradigm that synthesizes new images by selecting and stitching patches from
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real data. Zhao et al. (2024) further advanced this paradigm by proposing DQAS, an adaptive sampling strategy integrating active learning to refine class-inbalanced sample selection. This methodology addresses the limitations of traditional DQ approaches that assume uniform class distributions, enabling more efficient knowledge transfer across heterogencous network architectures. Similarly, Li et al. (2025) proposes ADQ to improve naive DQ by adaptively sampling data based on the representativenesee diversily, and importance scores of the generated bins.
3 Method
3.1
Image Processing with Latent Diffusion
Diffusion models learn generate images by iterative denoising a corrupted input through sequence of timesteps that estimate the distribution of training data (Dhariwal & Nichol 2021 Rombach al. 2022:
Saharia et al. 2022 Zhang al., 2023). For image processing tasks, the latent diffusion model (Rombach
ct al. 2022 is conditioned on a provided text prompt The training objective minimizes the latent diffusion
loss:
Bete,,l.e Eo (2,, (v)) 122.
(1)
where E(x) and (y(y) represent the latent embeddings of the target image and conditional prompt yr encoded by a pretrained autoencoder & and CLIP to (Radford al.) 2021) At each timestep, noise is added to the latent 24, and the denoising network Eø learns to predict the noise conditioned y.
Clustering- based Quantization
Submodular Sampling
Color Compensation
"[Sunnyl'
Bin 1
E
D
"[Snowy]]
DC3-Macaw
Bin 2
...
Cluster
Encoder
Denoising U-Net
Frozen
Decoder
Hue Prompts
ImageNet-
Bin N
Data Bins
Submodular Sampler
Figure 3: Pipeline of the DC3 framework. DC3 supports size-scalable, cesolution-variabel and architecture-adaalive dataset condensation for downstream tasks and even fine-tuning large vision mod- els (IVMs).
3.2 Color Compensation for Dataset Condensation
Our paper focuses on enhancing the color expression of condensed images. As illustrated in fig. con- ventional image processing pipelines (Plataniotis Venetsanopoulos 2000 Jähne 2005) suffer from color distortions due to insufficient semantic awareness and color reasoning capabilities. Most of the methods are implemented through mathematical transformations (Gonzalez) 2009). In contrast, diffusion-based image processing achieves pixel-level controllability through semantic-awar guidance, enabling precise under- standing and manipulation of color compensation, such as illumination adjustment and shadow harmoniza- tion. We provide guideline for hue prompt selection in the supplementary materials.
Inspired by aesthetic adjustments photographic post-processie we propose Color Compensationn, a mochanism intograting huc-related instructions into latent diffusion models improve color diversity, as well as alleviate the Color Homogenization during dataset condensation. Specifically, what we have are conditional images e₁ and set of hue prompts: P (C1, (c1,,2,.... Ct.) During compensation, the conditional
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image ey is fed into the pretrained latent diffusion model d along with randomly selected hue prompt et. The compensated image le is defined as:
L. = ((r,
(2)
A hue is the dominant color family of a particular color. In DC3, the huc prompts are categorized into cool (e.g., rainy, snowy) and warm (c.g., sunny, daylight),, except for the neutral hues (e.g., black, white, and gray). We select two hues from each group: (Pcost,Paani and implement the color compensation to synthesize (Lcoo1: !varn) according to eq. (2). Please refer to section C for a detailed guideline on prompt selection. Furthermore, we crop them in half and then stitch them to create a more informative image.
As illustrated in fig. color compensation effectively enhances dataset colorfulness by expanding the color gamut representation without introducing artificial artifacts, thereby contributing to model training downstream tasks. Compared to RDED (Sun et al., 2024), another crop-and-stitch approach that composites images through disparate image regions, our method synthesizes multiple colo-ccompensated variants of identical images. This strategy preserves structural consistency while promoting generalized representations across diverse visual domains, such as resolution and color distribution.
Transformation based Image Process
Diffusion- based Color Compensation
ColorJitter (033 0.3. 033 0.2)
ColorJitter (0.5, 0.5, 0.5, 0.3)
Colorlitter (0.9, as 0.8 0.5)
Warm Hue "Daylight"
Cool Hue 'Frazen Lake
Original Image
GrayScale N. of chaz]
GrayScale NLot.chas3
GaussianBlur (3,(0.2
Warm Hue
Cool Hue "Snowy"
Comparison between conventional and diffusion-based image processes. Left: Original image. Middle: Visualizations of conventional processing methods: (a) ColorJitter (brightness, contrast, saturation, hue), (b) Grayscale conversions (number of output chanmels), and (c) Gaussian blur (kernel, Diffusion-based color compensation. The cirtision-baseed approach achieves semantic-aware color reasoning, mitigating distortions caused by mathematical transformations in traditional pipelines.
Figure 4:
o). Right:
3.3 Quantization Analysis
DQ is emerging method for dataset condensation based on image-level selection. The idea of DQ is to partition data into different bins iteratively based on submodular gain, which was first proposed in GraphCut [Tyer et al. 2021) for coreset selection. The samples are selected from the bins randomly, whose dnstribution aligned with the original datasets, i.e., it does not have the color homogenization problem. Nevertheless, a fundamental limitation of this approach lies in its inherently monotonic decay of submodular gains across increasing bin indices, which inevitably causes random sampling to discard critical samples essential for model generalization. To reveal the relationship between submodular gain and sample distri- bution, we visualize the samples in ImageNette according to the values of their submodular gains with the help of t-SNE (Van der Maaten & Hinton 2008) plots (the mid-row of fig. Also, we provide additional visualizations for the first, second, and last. samples selected by the submodular gain criterion (the top row and bottom row of fig. 5).
Due to DQ performing random sampling within certain bin, the following situations exist: (a) Samples with high submodular gains may be excluded despite their distance from the optimal sample that has the
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The 1st and 2nd samples selected by Submodular Gain
Submodular Gain
* & * The 1 and last samples selected by Submodular Gain
Figure 5: Visualizations of the relationship between submodular gain and sample distribution. The following situations occur in traditional DQ methods: (a) inconsequential but compact samples may be selected because they are in different bins (bottom-left), and (b) essential and diverse samples may be discarded because they are in the same bins (top-right). Each column represents different category.
highest submodular gain (top-right in fig. 5 (b) Samples with low submodular gains may be selected even when proximate to the optimal sample (bottom-left fig. 5). Based on the visualization, we argue that the submodular gain of a sample is independent of its position the feature distribution. In other words, the sampling diversity does not necessarily benefit submodular gain.
To avoid selecting samples with small gains, propose clustering-based DQ, where the submodu- lar gain is then used as image sampler instead of bin generator. Specifically, the clustering al- gorithm divides the dataset into multiple clusters, which are naturally regarded as data bins (Cz). Each sample is iteratively assigned to the closest bins:
arg min || Cs11, j= 1, M.
3
As shown in fig. 5 data bins, also known clusters, are dis- persed throughout the feature space as expected. The cluster- ing algorithm performs a coarse partitioning of samples basod on feature-space similarities. Within each bin, we leverage sub- modular sampling to identify the representative samples with high submodular gains. In this way, the condensed dataset. preserves maximum semantic and feature diversity.
Figure T-SNE visualizations of intra-class clusters ImageNet. Sub- modular sampling selects the representative samples to preserve maximum feature di- versity. Fach column represents a different category.
3.4 Submodular Sampling
Color compensation enhances the diversity of the images, but we need to know which images require com- pensation, as compensating the entire dataset is computational. Thus, we introduce submodular sampling as feasible solution. The clustering-baser DQ naturally soparates the intra-class structure into different sample bins. Then, with the help of submodular sampling, we can select significant and diverse samples from the class.

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The samples with high submodular gain (G(z+)) will be sampled according to:
G(æx)) = Σ
ll(()) f(=x)11
Σ ll((r) f PES
(4)
PES
where [(p) is the feature of already selected sample and (xx) is the feature of the current sample 5*-1 is the sample set selected from the whole dataset D and 5k-1 Di,5k=1 is the complementary set. The submodular sampling is summarized in algorithm
Algorithm Submodular Sampling
Input: x E C: Data bins (Bins generated by clustering).
Input: M: Number of data bins.
Input: N: Image per class (IPC).
1: for Cj in C do
2: for zy in Cj do
3: Compute G(ax)) according to eq-
4: end for
5:
Sj = 0
C = sort(C,, desc)
if N < M then
M N
end if
10: Sj = Cj(0: -j=][(: N/M]
# Selection
11: end for
12: S 5 S Sj
Output: S: The selected sample set.
To sum up, DC3 quantifies sample-wise contribution through submodular gain and improves sample perfor- mance with Color Compensation, ensuring that the compressed dataset remains both representative and diverse across categories. By effectively preserving critical information, our approach enhances the overall quality and utility of the condensod datasct. The entire DC3 pipclinc is summarized in fig. 3
4
Experiments
4.1
Experimental Settings
We perform systematic evaluations across varying-scale benchmarks to assess generalization boundaries. For large-scale datasets, we include ImageNet-1K (Deng al. 2009) (224 x 224) and subsets, such as Tiny-ImageNet. (64 64). Small-scale low-resolution (32 32) analysis utilizes CIFAR-10/ (Krizhevsky et al. 2009). To quantify task difficulty sensitivity, benchmark ImageNette and Image Woof, the subsets of ImageNet with 10 classes. Note that ImageWoof poses greater discrimination challenges due to more inter-class similarity. We use Stable Diffusion-V1.3 and DIT-XL/2-256 as our foundation models.
Following the prior works Sun et al. 2024 Chen et al. 2025 J we set IPC to 1, 10, and 50. For a fair comparison, we adhere to the official implementation of RDED (Sun et al., 2024 and employ a ResNet-
network to generate soft labels for fine fine-grained supervision. Performance validation is conducted using Torch 8x NVIDIA 3090 GPUs. Detailed settings discussed in the supplementary materials.
4.2 Comparison with SOTA Methods
While our method draws inspiration from DQ, we exclude direct comparisons due to differing evaluation prioritics. Specifically, DQ emphasizes generalization across large IPC keep ratios, whereas our focus lies in optimizing compression performance through color compensation low IPC regimes. Consequently, we benchmark against. DD such the matching-based TESLA (Cui et al. 2023 SRePT. (Yin et. al. 2023

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DataDAM Sajedi et al. 2023 RDED Sun et al. 2024 and generation-baset Minimax (Gu al. 2024
D'M (Su et al. 2024), and TGD Chen et al. 2025), a aligning with the objective of DC that maximizes dataset utility under extreme compression. Importantly, our DC3 inherits all the advantages of DQ while achieving superior compression efficiency.
ImageNett family. The extension of dataset condensation to large-scale scenarios represents a critical advancement in DCAI research. Our method demonstrates superior scalability and effectiveness on this challenging task. As evidenced by table [ DC3 achieves SOTA performance across all IPC settings large-scale ImageNet- IK. Notably, DC3 delivers 8.4% Top-1 accuracy improvement = 10, surpassing the SOTA results on large-scale dataset. This performance gap underscores its superior ability to represent complex visual patterns, particularly in high-resolution large-category scenarios.
RDED at IPC
Matching based
Generation- cnneration-based
Dataset
IPC
Full
TESLA
SRe²L
RDED!
Minimax
D
IGD
DC3
7.7
0.1
6.6
8.1
ImageNet-1K
10
17.8
21.3
42.0
44.3
27.9
46.2
50.4
69.8
50
27. 27.9+1.2
46.8+0.2
56.5+0.1
58.640.3
55.2+0.4
60.340.4
62.3
Top-1 Accuracyt ImageNNet-1K. The results of DC3 and other state-of-the-art methods evaluated on ResNet-18. f: The results of TESLA are evaluated on ConwNet.
Table 1:
Furthermore, we validate DC3 efficacy on some challenging domain-specific ImageNet subsets. As listed table 2 our approach achieves 16.4% accuracy gain over IGD at IPC-10 on ImageNette, For the more complex Tiny-ImageNet (200 categories), our method has better performance than others, demonstrating consistent performance across varying task complexities.
Dataset
IPC
RDED
Minimax
D*M
IGD
DC3
1
20.8+1.2
23.2+1.4
ImageWoof
10
38.5421
37.640.9
42.9
47.2+16
48.740.1
50
68.5+0.7
57.1+0.6
62.1+0.4
65.4+1.8
72.440.6
1
35.8+100
32.1+0.3
34.1+0.9
37.640.9
ImageNette
10
61.4+0.4
62.0+0.2
68.440.
66.2
84.8+1.1
50
80.4
76.610.2
81.310.1
82.0
89.8
1
9.740.4
13.340.8
15.1+02
20.0+1.2
ImageNe
10
41.9+0.2
39. 39.2+0.7
35.7+06
45.1+1.1
50
58.2 58.2+a.1
44.84u.2
46.2+11
59.4
Table 2: Top-1 Accuracyt on ImageNet subsets: Image ImageNette, and Tiny-ImageNet. All of the results are evaluated by ResNet-18. DC3 holds the optimal performance among datasets and methods.
The Image subset, known for its high intra-class similarity, poses challenges for dataset condensation. Nonetheless, our method achieves the best performance this benchmark. The success can be attributed to two principal factors: (1) color compensation mechanism effectively preserves subtle discrimina- tive features that are critical for distinguishing similar classes, and (2) the submodular sampling strategy ensures optimal coverage of diverse intra-class variations, preventing the loss of essential patterns during condensation.
CIFAR-10/100. To assess the generalization across varied scales and resolutions, we also evaluate DC3 on CIFAR-10 and CIFAR-100. As shown table 3 our approach exceeds those of previous SOTA models by significant margin the small-scale benchmarks. The accuracy at TPC-10 is 1.5x of our baseline
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RDED. For the more challenging CIFAR-100, 100-class fine- fine-grained dataset, method maintains strong competitiveness across different IPCs, demonstrating robustness to both coarse and fine-grained classification tasks at low resolutions.
Datasct
IPC
RDED
L
DataDAM
D*M
DC3
10.9
16.9
32.0
17.61111
25.611.2
CIFAR-10
10
30.3
27.2+0.5
54.2+0.8
51.540.
57.820.8
50
+2.1
47.5+0.6
67. 67.0+0.4
62.3+0.2
80.9
11.0
2.0+0.2
14.3
5.7=1.4
21.1=1.6
CIFAR-100
42.6
23.5
34.8
44.040.
57.4
50
62.640.1
51.440.8
49.4+0.3
48.4+0.9
64.2=0.5
Table 3: Top-1 Accuracyt on CIFAR-10 and CIFAR-100. The results are evaluated by ResNet-18.
Last but not least, we find that the existing dataset condensation methods exhibit varying degrecs of perfor- mance degradation under different scales, class reumbers, and struclures. DC3 enhances the data colorfulness and inherits the generalization performance of DQ, successfully overcoming this adaptation challenge.
4.3
arosttarchite Generalization
To support the claim that pixel optimization methods, which cause "color homogenization", impair model generalization, direct comparison is made between DC3 and various matching- based DD methods (such as KIP (Nguyen et al. 2021b), DSA) on "Seen" and "Unseen" network architectures. The results in table 4 indicate that the aforementioned methods experience a sharp accuracy decrease when evaluated on archi- tectures not previously seen. Such significant performance degradation provides strong empirical support for the hypothesis that "color homogenization" hinders model lcarning of transfcrable data representations. Among all methods, DC3 and DSA demonstrate the most robust generalization to the unseen architecture, exhibiting the slightest performance degradation. Crucially, DC3 absolute performance has far exceeded that of other methods.
Eval. Model
KTP
DSA
MTT
TESLA
DC3
Seen
47.2
53.0
65.3
66.4
84.8
Unseen
15.9 J31.3
31.9
34.6
34.8
60.4
Table 4: Different DD methods show performance degradation on the "unseen" architecture. Here, "scon" architectures refer to CNN-based models (c.g., ResNet-18 or ConyNet-D6)), while "unseen" architectures are Transformer-basell (e.g., Swin-V2-T ViT-S). We choose the worst result. of DC3 on Swin-V2 V2-T under IPC-10, but it still performs the best among methods.
To evaluate the cross-architecture generalization of compressed datasets, we deploy them to CNN-based (ResNet series, MobileNet-Ve V2, EfficientNet-BO) and Transformer-basst architecture (Swin-V2-T), We em- ploy ResNet-18 as the feature sampler during submodular sampling, thus, we make it the teacher model naturally. As demonstrated in table 5. ResNet-18 achieves 84.8% Top-1 accuracy at IPC-10, while Swin-V2. T exhibits a significant drop to 60.4%. This discrepancy stems from fundamental architectural divergences: CNNs rely on hierarchical feature extraction through localized receptive fields, whereas Transformers model the long-range dependencios via global attention mechanisms. Such structural differences lead to varying sensitivities to high-frequency details in compressed data. Despite inherent architectural biases, Swin-V2-T achieves 80.0% accuracy at IPC-50, marking a 19.6% improvement over IPC-10, which highlights its potential for archi-archi applications.
Notably, the performance volatility (standard deviation) approximately 3.93% (IPC-50) across models ranging from lightweight. MobileNet-V2 to deep ResNet-101, demonstrating its robustness to model scale.

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Sampler
IPC
ResNet-18
ResNet-50
ResNet-101
MobileNet-V2
Efficient
Swin-V2-1
ResNet-18
10
84.8
112
80.8
79.0
81.8
84.2 1.4
60.4
50
89.8 40.3
89.0
89.2
89.6
90.2
80.0
Table 5: Generalization performance of DC3 across different neural network architectures. The results demonstrate the robustness and adaptability of the condensed datascts to various models.
Additionally, DC3 achieves the best accuracy of 90.2% on EfficientNet-BO, which also validates its align- ment with efficient network designs. These findings not only confirm the cross- cross-architecture and cross-scale generalization of DC3 but also elucidate the coupling mechanism between dataset compression and model inductive bias.
4.4 Ablation Study
This section addresses pivotal question: Does the powerful distillation capa- bility of DC3 stem from the inherent data augmenta- tion ability of the diffusion model itself, or from the specific color compensation sirulegy designed to address the color homogenization problem? For this purpose, we design scries of ablations on dif- fusion model schemes with three different kinds of prompts. The first is a "null" prompt, providing information performance baseline. The second is the "HR" prompt, which uses "High- Righ-RResouutin im- ages" as prompt words to evaluate the inherent effectiveness of the diffusion model itself. The third is the carefully designed DC3 color compensation prompt. guideline on how to select DC3 prompts is provided in section C The results in table demonstrate that the DC3 strategy achieves optimal performance on both the CIFAR-10 and CIFAR-100 datasets, regardless of the IPC settings, which confirms that the perfor- mance improvement is not simply due the introduction of diffusion models, but attributed to the proposed color compensation strategy. This strategy effectively enhances the color diversity of the compressed data, solving the common issue of color homogenization in previous methods and improving the final performance of the surrogate model.
Color compensation.
Dataset
IPC
Null
HR
DC3
10
50.3
54.5+0.2
57.8+0.8
CIFAR-10
50
73.6
76.040.2
80.9
10
51.8
53.510.2
57.4
CIFAR-100
50
58.3+0.1
61.2+0.1
64.2
Table Comparison results of different prompts.
Compensation manners. To further validate the effective- ness of color compensation. we introduce the colorfulness metric (Hasler & Suesstrunk, 2003), which reflects the color richness in an image and helps establish a correlation between experimental metrics and the color attributes.
geNette
ACC
Caleniieess
Top
Tup
Specifically, through the red-green (rg) and yellow-blue (yb) difference of an image, the colorfulness can be defined as:
Original
Driginal Near
Figure 7: Ablation study on color com- pensation. The colorfulness and accuracy results highlight the enhancement color diversity with the proposod diffusion-basod color compensation technique.
colorfulness Goott + 0.3 x Proots
(5)
where Groot and Proot represent the combined standard devia- tion and mean of the rg and yb differences, respectively:
Troot
V Vol
Prg + oz
Proot
M2g + 13
(6)
In eq. 6 = R Gl and yb = 0.5 x R G) - B are the differences between the Red, Green, and Blue components of an image.
As depicted in fig. 7 the application of color compensation to submodular sampplllla--apeed datasets demonstrates significant thcoretical advantages over uncompensated approaches. The compensation strategy addresscs the inherent color distribution imbalances in compressed datasets by enhancing the color of underrepresented re- gions while maintaining visual naturalness. The cool hue specifically targets myan-blue-magenta deficiencies.
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while warm compensation addresses vellow-orange-red limitations. Thus, the mixed compensation provides comprehensive spectral coverage. Section D discusses the different mix methods in detail.
Compensation v.s. augmentation. To comprehensively evaluate the dual contributions of our proposed DC3 framework in color compensation and coresct selection, a detailed ablation study is conducted with results listed table 7 Since one of the primary contributions of DC3 addressing the issue of color homogenization in existing methods, table 7 directly compares DC3 against other color augmentation techniques such as Color Jitter, HSV Shift, Balance Tuning, and AutoPalette (Yuan et al.) 2024 The results demonstrate that DC3 significantly outperforms these alternative color solutions on the CIFAR-10 and CIFAR-100 datascts, thus confirming the unique advantage of the diffusion-based approach in enhancing color diversity. We named this strategy compensation instead of augmentation to distinguish it.
Dalaset
Color Jitter
HSV Shift
Balance Tuning
AutoPalette
DC3
CIFAR-10
73.511.8
24.2
74.611.4
79.4
80.910.5
CIFAR-100
53.2+1.6
53.7=1.4
54.411.7
53.3
64.210.8
Table 7: Comparison with augmentation methods under IPC-50. For fair comparison, we substitute Colour Compensation with augmentation methods, while the rest of the pipeline remains identical.
Submodular sampling. The upper section of table S reveals that DC3 with clustering- submodular sampling performs better across CIFAR-10 and ImageNette under varying IPCs. The lower section only changes the sampling strategy, while the rest of the components remain fixed, including color compensation.
The competitive results of DC3 attributed the synergistic combination of clustering and submodular sampling. Clustering-base based bin generation creates coherent and representative data bins by minimizing intra-cluster distances, providing stronger foundation for the subsequent sampling process. Submodular sampling then optimally selects diverse and representative samples from these well-formed bins, capturing the underlying data distribution while maintaining diversity. This integration addresses the challenge in dataset condensation: preserving essential information while significantly reducing dataset scale.
CIFAR-10
ImageNette CIFAR-10
ImageNette
Method
Color Compensation
IPC = 10
IPC = 50
DQ
30.7
69.3
50.5+0.4
74.2+0.5
x
DC3
49.4 10.71a0
75.3 Lo.6t.
71.4 10.5120
79.6
DQ
51.2
81.841.5
68.4
85.340.6
DC3
57.8
84.8 8.18.
80.9 10.5112.6
89.8 20.214.6
Table 8: Ablation study on sampling strategics. DQ randomly selects samples within data bins, while DC3 utilizes submodular gain as the sampler.
Meanwhile, to validate the effectiveness of the improved submodular sampling selection st.rateyy, table g compares DC3 with various commonly used coreset methods, including Herding, k-OG, GradMatch, and submodular-based DQ methods. The results show that DC3 achieved the best Top-1 accuracy on all tested datasets, particularly outstanding on CIFAR-10 (80.9)), CIFAR-100 (64.2%), and Tiny ImageNet (59.4%).
Overall, the above ablation experiments demonstrate that both color compensation and clustering-basee submodular sampling in the DC3 framework are superior to existing technologies. It not only effectively improves the information density and representation ability of the compressed dataset but also provides an effective solution to the key bottleneck in dataset condensation.
Pipeline analysis. The ablation study on the DC3 pipeline reveals significant performance improvements by combining color compensation and submodular sampling. As listed in table IO standalone submodu- lar sampling improves accuracy by 10.9%, while color compensation achieves 15.1% on CIFAR-10. Their synergistic integration yields a 19.3% improvement. Similar trends hold on ImageNette.
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Method
CIFAR-10
CIFAR-100
Tiny- ImageNet
Herding
34.8
34.4
45.7
k-CG
31.1
42.8
46.3
GradMatch
30.8
42.8
43.2
DQ (Submodular)
50.5
45.9
52.8_
DC3
80.9
64.2
59.4
Table 9: Comparison with other selection methods under IPC-50.
This synergy arises from complementary mechanisms: color compensation mitigates chromatic homogenization by restor- ing natural huc distributions, while submodular sampling max- imizes feature diversity. The former ensures perceptual fidelity critical for color-sensitive tasks, whereas the latter optimizes representativeness by prioritizing samples that span the data manifold. Their joint operation enforces a compressed dataset that aligns with both low-level chromatic statistics and high- level semantic structures, thereby bridging the gap between pixel-level optimization and image-level selection.
S
CIFAR-10
ImageNette
38.5
55.8
V
49. 49.410.3m0
75.3
V 53. 53.640.6tus
77.9
/ / 57.8 10.8
14.1811.1990 199.0
Table 10: Ablation study on the impact of submodular sampling (S) and color com- pensation (C) strategies.
4.5 Training diffusion models with DC3
Besides being effective on downstream tasks, we argue that the condensed datasets should adapt to generation tasks. We fine-tune the Stable Diffusion (SD) and Diffusion Transformers
MAXDO
(DiT) (Peebles & Xie
2023) the original and mixed (origi-
nal and DC3-condensed IPC-50 data) ImageNetle datasel with
10.44
2009
72:5
LoRA (Hu et al. 2022 and DiffFit (Xie et al. 2023).
W0 DIA LaV MOCCIT 30.200 STAN LAWL 2000I10
The results drawn in fig. S show that datasets generated by DC3 effectively support the fine-tune pipelines without model collapse. Specifically, the models fine-tuned by mixed data achieve better FID-IS (Salimans et al. 2016 Heusel al. (017)) scores compared to those the original dataset and baselines, indicating better alignment with real data distributions. This performance gap stems from its ability to preserve feature diversity by chromatic alignment and submodular sampling, which maintains essential data manifolds while removing redundant information.
Figure 8: Fine-tuning diffusion models with DC3. To validate the effectiveness of our method, cach finc-tuned model gener- ales 10,000 images according to the number of samples in ImageNette (12894).
5 Conclusion
We present DC3, simple but efficient way optimize both the performance and generalization of con- densed datasets. Based our color compensation mechanism, the experiments demonstrale favorable results across wide range of datasets with various recognition difficulties. Notably, DC3 proves information preservation mechanism within condensation, validating the feasibility of pre-training large vision models on condensed datasets without model collapse.
Limitation and future work. Although DC3 a training-fres approach, the compensation process is still constrained by the inference speed of the diffusion model. Future directions will focus on extremely efficient DC under extreme compression. We hope this work will inspire further research into dataset condensation. Perhaps in the future, the scaling law will be broken to linear level with the help of Data-Centric AT.
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Acknowledgments
This research was supported in part by JSPS KAKENHI Grant Numbers JP24K23849 and JP25K21218.
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Appendix
A Experimental Details
The collaboration between sampling and compensation enables DC3 to possess powerful condensation capabilities. There are details of these pipelines that require explanation. In submodu- lar sampling, section presents the results for different cluster numbers (K), as K significantly affects the sampling results. Also, in the color compensation phase, section C introduces the selection guideline of hue prompts. Additionally, section [ experiments on different crop-and-stitch methods to further en- hance the information density for the condensed images.
IPC-10
IPC-100
PPC-200
94
92
90
88
86
Top-1
84
82
Number of Clusters (K)
Figure 9: Top-1 Accuracyt of the number of clusters (K) for varying IPCs (IPC-10, 50, 100, 200). The results demonstrate the impact of cluster granularity on classifica- tion performance.
During the validation stage, the parameter details of all ex- periments are listed in table I1 Finally, we provide a wealth and H including condensed images and fine-tuned DíT -enereeer images, which prove the offectivencss of DC3. For DC3 and the reproduced basclines,
of visualization ions in sections
we report the mean and standard deviation over three runs. For other competing methods, we quote the results directly from their original publications.
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Settings
Values
Settings
Values
Settings
Values
guidance scale
4
guidance scale
guidance scale
4
network
Net18
network
ResNet18
network
18
input size
224
input size
32
input size
224
optimizer
AdamW
optimizer
AdamW
optimizer
AdamW
learning rate
0.001
learning rate
0.001
learning rate
0.001
weight decay
0.01
weight decay 0.01
weight decay 0.01
(a) ImageNet
(b) CIFAR-10 and CIFAR-100
(c) ImageWooff and ImageNette
Table 11: Evaluation details for different datasets.
B Selection of Cluster Numbers
The number clusters (K) should chosen carefully. Thus, we conduct experiments using four clustering settings: = 1, 5, 10, and 20. As depicted fig. 9 the Top-1 accuracy reaches an optimal value at K
10. According to fig. 9 we can also claim that DC3 not sensitive to K, since the accuracy drops slightly at other K settings.
C Guideline of Hue Prompt Selection
DC3 attempted implement pixel-level optimization to mit- igate color homogenization using instruction-oondttineed diffusion models. Cool and warm hues group the instruc- tions. This laxonomy originates from quantitative analy- sis of natural lighting in photographic aesthetics: cool-hue scenes exhibit spectral energy concentrated in short wave- lengths (400-500 nm), whereas warm hucs correlate with long- wavelength distributions (550-700 nm). As listed table 12 cool-hue prompts (e.g., "rainy", "snowy") ") align with low-color- temperature scenes (c.g., blue-cyan hue in rain/snow). In con- trast, warm-hue prompls (e.g., "sumny", "golden hour") cap- ture high-color-temperature spectral characteristics (e.g., orange-yellow hue under strong illumination). By embedding these physically grounded semantic instructions into latent diffusson models, we successfully implement pixel-level color compensation (e.g., adjusting illumination while preserving structural integrity).
Cool Hue
Warm Hue
rainy snowy
sepia sunny
infrared underwater frozen lake
daylight
vivid colors
golden hour
Examples of instruction prompts Table 12:
for cool and warm hues.
The selection strategy adheres to the following criteria: (a) visual discriminability terms must induce color shifts aligned with human perception (e.g., "underwater" implies blue-green hue, "vivid colors" " triggers saturation enhancement), (b) complementary coverage prompts should span opposing color families to maximize gamut diversity. In our experiment, randomly selected one prompt from both cool and warm hues for color compensation. Then we crop-and-stitch the generated image to create single distilled image.
D Ablation on Crop-and-Stitch
We appraise three kinds of atthit methods datasets with different resolutions. The experimental results listed in table 13 demonstrate that crop-and-stitch in half yields the best results across resolutions.
Excessive stitching (e.g., grid stitch and extreme pixel stitch) will degrade the semantic integrity, impair- ing feature extraction capabilities. Pixel stitch exhibits unstable performance due to insufficient spatial continuity constraints. Half merge effectively preserves structural semantics while cnabling information fu- sion, particularly advantageous for cool/ //arrmmmmmmmmmmmmm hue compensation tasks. By maintaining complete half-region structures, this method retains coherent color distribution patterns, thereby enhancing discriminative learn-
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ing of chromatic features. The accuracy performance validates half- melfe the optimal strategy for image fusion in DC3.
Datasct
IPC
2
x
2
1
4 x
50%
Pixels
8 8 Grids
16 x 16 Grids
25.6
22.4
24.9
23.0
19.5
CIFAR-10
10
57.8
58.2
54.6
56.1
44.2
1
15.2
12.6
12.8
15.0
10.2
ImageWoof
10
38.0
35.6
37.6
34.4
37.4
1
37.6
32.2
28.8
36.4
27.8
ImageNette
10
84.8
81.7
76.4
75.8
77.1
Table 13: Ablation results on different stitch methods. n 4: Crop of each of the n compensated
images and then stitch. Pixels: Randomly extract 50% of the pixels from compensated images and then stitch. Grids: Divide each of the 2 compensated images into grids, and then randomly stitch half of the grids into complete image.
E Visualization Analysis
As a performance matching method, the KDE curves of MTT are different from those of other matching methods. From fig. [Ta] the distribution reveals a leptokurtic profile characterized by a peaked central tendency and attenuated tails. This statistical property indicates significant Color Redundancy, where the compressed dataset over-represents narrow chromatic ranges while under-sampling critical color vari- ations. Both Color Redundancy and Color Homogenization phenomena compressed data manifolds inadequately span the original color space, thereby violating the manifold hypothesis essential for dataset generalization. In contrast, DC3 fits the color distribution well on CIFAR-10 (fig, 10b and ImageNette (fig. 10ch and achieves superior performance.
Charnd
Chamcc
Nomrol
(a) MTT
(b) DC3 (Ours)
Charpe R
ChanteG
Cramell B
Nor morceleed 7ool Yolus
hommalee Ned ValLe
Norma und Floe Value
(c) DC3 (Ours)
Figure 10: The KDE curves of the normalized RGB pixel value of MTT and DC3. (a)-(b) CIFAR- 10 results on MTT and DC3. (c) ImageNette results DC3.
F Comparison of Computation Time and GPU Memory Cost
Table 14 evaluates the computational efficiency of the DC3 framework, comparing its Color Compensation
stage (left table) and Bin Generation stage (right table) against relevant state-of-the-art methods. The results indicate that even when processing high-resolution (512 x 512) images, DC3's time consumption (4.1s) and GPU memory footprint (2.7GB) are significantly lower than those of methods such TESLA and L. Concurrently, the right table shows that its Bin Generation efficiency marginally superior the conventional DQ method. In summary, DC3 not only surpasses existing methods in performance but
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Published in Transactions on Machine Learning Research (10/2025)
also demonstrates substantial advantage in computational cost, proving the framework's high practical feasibility and efficiency in achieving high-quality dataset compression.
Method
Resolution
Time(s))
GPU(GB)
Method
GenBin(h))
TESLA
64 X 64
46.0
13.9
DQ
1.0
MTT
128 x 128
45.0
79.9
DC3
0.9
SRe
224 x 224
5.2
34.8
DC3
512 x 512
4.1
2.7
Table 14: Comparison of computation time and GPU memory cost.
G Visualizations from Fine-tuned DiT
From the experimental results shown in the main text and fig. I1 we believe that DC3 does not cause model collapse after fine-tuning DiT for 50 epochs. Furthermore, using DC3-compressod data as an auxiliary training set yields semantic-consistent generation results.
This effectiveness stems from the ability to maintain fidelity and enhance the feature discriminability of DC3, which aligns the latent space of condensed data with natural image priors using diffusion models. This work also pioneers the direction of fine-tuning the generative models using compressed datasets. The across-architecture generalization results in the main text further validating the practicality of our approach for data-efficient transfer learning.
More Visualizations
We randomly sample the images from the DC3 condensed CIFAR-10/100 100, ImageWoof, and ImageNette. The visualizations are depicted figs. 12 to
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Published in Transactions on Machine Learning Research (10/2025)
RA
(b)
Figure 11: Comparison of images generated by ImageNette fine-tuned DiT with different settings. The first row displays outputs from DiT without fine-tuning. The second row visualizes the results from the model fine-tuned with the original data, while the third row presents outputs from the mixed data (original ImageNette and DC3 condensed IetteNette IPC-50) fine-tuned DiT.
Figure 12: More visualizations selected from the condensed CIFAR-10.
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Published in Transactions on Machine Learning Research (10/2025)
Figure 13: More visualizations selected from the condensed CIFAR-100.
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Published in Transactions on Machine Learning Research (10/2025)
Figure 14: More visualizations selected from the condensed ImageWoof.
Figure 15: More visualizations selected from the condensed ImageNette.
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Reviews:
[{"review_id": "Oxl9C6oTjT", "reviewer": "Reviewer_hcBN", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This manuscript introduces DC3, a data augmentation technique, applied to the task of data condensation. This framework combines clustering-based quantization with submodular sampling and color compensation using a diffusion model. The main claims are that other data condensation techniques generally suffer from color homogenization, and DC3 solves this problem by color augmentation using the Latent Diffusion Model (LDM). However, it is not specified which model is used. The model is presented as efficient and training-free. Experiments performed on CIFAR, ImageNet, ImageWoof, and Tiny-ImageNet show that DC3 outperforms state-of-the-art methods for data condensation.", "claims_and_evidence_yn": "No", "claims_explanation": "1 Ambiguity of diffusion model\n\nThe paper only mentions using a Latent Diffusion Model (LDM) but does not explicitly state which model or checkpoint is used. While Section 4.5 (experiments) mentions that two diffusion models (Stable Diffusion and Diffusion Transformers) are trained, Section 3 (method) fails to mention which model was used to generate new images for color variation. While the citation to Rombach et al. 2022 suggests Stable Diffusion, this should have been stated clearly for reproducibility.\n\n2 Effective dataset size inflation\n\nReported IPC budgets (e.g., IPC-10 or IPC-50) are misleading. In practice, DC3 multiplies the dataset size by generating more color-compensated variants per selected sample. For example, at IPC-10 on ImageWoof, instead of training with 10 images, the final dataset contains 100 images due to warm/cool prompt variations (Table 11). This undermines fair comparison to methods that strictly adhere to IPC budgets.\n\n3 Unaccounted computational cost\n\nThe method is described as comutational efficient, yet generating hundreds of diffusion-augmented samples per class is computationally heavy. The paper omits reporting inference cost, runtime, or GPU resources required for augmentation, which are critical for evaluating practicality. The main objective of dataset condensation is that we have a very large database, and it is necessary to reduce it intelligently in order to reduce the training time and reduce the computational cost.\n\n4 Potential information leakage from diffusion model\n\nThe diffusion backbone (trained on large external datasets) likely introduces external knowledge into the augmented data. This could explain strong cross-architecture generalization, but it blurs the line between dataset compression and data augmentation with external priors. The paper does not acknowledge or analyze this risk.\n\n5 Lack of theoretical grounding\nThe paper provides no formal justification for why color diversity should be so crucial to generalization. Claims about submodular sampling and color compensation remain empirical observations (KDR curves) without deeper theoretical analisis. Correlation does not mean causation.\n\n6 Evaluation scope\n\nComparisons to recent Dataset Quantization (DQ) methods (ADQ, DQAS, 2024–25) are missing. These methods are natural baselines given DC3’s use of clustering and quantization. The method is also not tested at moderate keep-ratios (10–30%), where DQ methods excel, making it unclear whether DC3’s advantage holds beyond extreme IPC settings.", "audience_yn": "Yes", "audience_explanation": "Although the manuscript makes several omissions and is not exhaustively tested, it presents very good results compared to other models; however, this should be taken with caution.", "requested_changes": "no", "broader_impact_concerns": "no", "additional_comments": "Clarity: Figures and explanations (e.g., Algorithm 1, submodular gain) could be made clearer; currently, the pipeline is somewhat difficult to follow.\n\nFairness of baselines: Since DC3 augments its samples via diffusion, other baselines should be allowed similar augmentation to ensure fairness.\n\nApplicability: The method is well-suited for low-data augmentation scenarios (e.g., medical imaging, rare-event domains), but less compelling as a dataset condensation method where the main motivation is computational efficiency.", "extra_scores": {}, "extra_text": {}}, {"review_id": "5Jk9Iz0kQj", "reviewer": "Reviewer_JoLv", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "## Summary\nThis paper prpoposes a new Dataset Distillation method: DC3 that makes use of Dataset Quantization (DQ) and augmentation via a diffusion model to condense a dataset into a smaller one. Previous approaches that are based on selecting samples from the dataset (basically DQ and Coreset Selection methods) have mostly been proposed for a scenario where a percentage of the dataset is retained (at least 10%). DC3 modifies the basic DQ approach primarily by doing 2 things: i) using clustering for forming bins in DQ and ii) augmenting the selected samples using diffusion models. The paper demonstrates that the proposed pipeline outperforms the baselines in low IPC (Images Per Class) settings and generalises across architectures.\n\n### Strengths\n- The pipeline is simple and modular.\n- Pretrained diffusion models are used in a novel manner to augment the selected subset of samples.\n- The selected subset of samples, while being small, generalises across architectures.\n\n### Weaknesses\n- The standard deviation and details around training models using the condensed datasets are missing.\n- Some sections/figures need more clarification and details.\n- Depending on the details that are missing, more ablations might be required.", "claims_and_evidence_yn": "Yes", "claims_explanation": "My major concerns/questions/requests are the following:\n\n1. **Standard deviation across random seeds** Please report standard deviations for all numeric results across multiple random seeds. As of now, it's unclear whether the reported results are for one best-performing model trained on the respective condensed dataset or an average across runs. Please also report how the models are trained and how the best checkpoint (for reporting numbers on the test set) is selected for every run and every method.\n\n2. **Page 10 — “Compensation vs. Augmentation” protocol.** When comparing Colour Compensation to Augmentation, do you only replace the Colour Compensation module while keeping the rest of the DC3 pipeline unchanged? If not, then that would be an ablation that is required.\n\n3. **Page 11 — submodular sampling ablation protocol.** For the submodular sampling variant, please state explicitly whether (a) Colour Compensation is omitted and clustering is applied only within Dataset Quantisation (DQ), or (b) Colour Compensation remains and only the sampling strategy is replaced. Another ablation required would be the one that complements the one in point 2: modify only the sample selection strategy while keeping the rest of the pipeline the same.", "audience_yn": "Yes", "audience_explanation": "Dataset Distillation has gained popularity over the years (https://github.com/Guang000/Awesome-Dataset-Distillation), and this manuscript explores a less explored direction of selecting samples from the dataset to condense it while keeping the total number of selected samples low. I think the dataset distillation community will find this paper intriguing, not only because it advances SOTA but also because of the novel pipeline and for showing that one can achieve high accuracy even with sample selection (Dataset Quantisation).", "requested_changes": "### Critical for securing my recommendation for acceptance\n\n1. **Fig.5** I did not fully understand Fig. 5. Does each subplot show a different class (label)? And what you're trying to show is that the clusters are spread out?\n\n2. **Fig.6** I did not understand Fig. 6. What do the different columns signify? And are you trying to show that samples should be as far apart as possible, but they are not? You have pointed out some of the subplots in the main text but the overall idea behind the figure is unclear. Also, in the middle, all the submodular gains are very spread out and do not seem to follow a pattern so its unclear how the colouring scheme is helping.\n\n3. **Page 11 / Table 9 — what does “DC3” denote, and what is the IPC here?** In Table 9, does the label “DC3” refer to the full DC3 pipeline or only a subset of components? Also, please report the images-per-class (IPC) used for each row in the table caption.\n\n### Not critical for securing my recommendation for acceptance, but should ideally be fixed\n\n1. **Abstract — clarify “inefficiency condensation”.** Do you mean *“inefficiency in condensation”* or *“inefficient condensation”* or something else?\n\n2. **Abstract — expand abbreviations on first use.** Please write out the full form of every abbreviation the first time it appears in the manuscript (for example, *Frechet Inception Distance (FID)* in the abstract).\n\n3. **Page 3, line 1 — unclear sentence.** I did not understand the sentence *“We propose DC3 that utilizes the compression ability as long as the generalization from different dataset condensation methods.”*. Please reword this sentence for clarity.\n\n4. **Page 4, §3.1 — missing citation for CLIP.** CLIP is referenced in the notation for latent diffusion conditioning but lacks a formal citation. Please add the CLIP citation where it is first mentioned.\n\n5. **Page 4, 2nd last line — clarify “except for the neutral colors”.** I did not understand what you meant by the phrase *“i.e., except for the neutral colors”* in the context of the whole sentence.\n\n6. **Page 5, last paragraph — define “optimal sample”.** The manuscript refers to an *“optimal sample”* without a formal definition. Please define this term precisely (for example, as the sample maximizing a specified submodular gain or distance metric).\n\n7. **Missing short description of RDED.** RDED is used as a baseline and as part of the narrative in the experiments section, but no short, self-contained description is given.\n\n8. **Section 4.1 — use of soft labels and ResNet18.** It is unclear whether soft labels are applied only to RDED or uniformly to all methods that require soft labels. Same for ResNet18.\n\n9. **Table 4 — define “seen” and “unseen” architectures.** What all architectures are included in *“seen”* and *“unseen”* sets?\n\n10. **Page 12, §4.5 — expand and cite FID-IS.** The term *“FID-IS”* is used without definition or citation. Please expand the abbreviations on first use (e.g., *Frechet Inception Distance (FID)*, *Inception Score (IS)*) and cite the original references. Note: you may want to cite FID in the abstract and then simply use it here directly, but IS does need a citation and expansion.", "broader_impact_concerns": "There are no concerns on the ethical implications of this work.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "xphelJhEGO", "reviewer": "Reviewer_YKTQ", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper studies the trade-off between performance and fidelity in dataset condensation. Unlike prior works that primarily rely on quantization or distillation, this work highlights the often-overlooked color issue. To address this, the authors introduce a diffusion-based color compensation strategy (DC3) along with a submodular sampling approach. The method is intuitive and demonstrates superior performance on both traditional classification tasks and diffusion-based image generation.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The paper provides a substantial amount of experimental evidence, including cross-architecture evaluations (ResNet, MobileNet, Swin-Transformer), comprehensive ablation studies on both the color compensation strategy and submodular sampling, and demonstrations on both classification and generative tasks. These results strongly support the claim that the proposed method improves upon traditional dataset condensation methods in terms of accuracy and generalization.\n\nHowever, some claims are not as clearly substantiated. For example, the KDE curve analysis (Fig. 2) is difficult to reconcile with the stated claim that the distribution is “closer to the real dataset,” since the density curves diverge notably in marginal and central bins. Similarly, certain qualitative results—such as the synthetic images in Figs. 12–15 that appear visually unnatural, and the minimal visual differences in Fig. 11—make it less clear whether fidelity is truly preserved. These issues do not invalidate the contributions but do weaken the strength of some claims, particularly those concerning dataset fidelity and distributional alignment.\n\nOverall, the evidence is convincing in demonstrating the method’s effectiveness (e.g., performance gains and efficiency), but somewhat less convincing in proving its fidelity to the original data distribution.", "audience_yn": "Yes", "audience_explanation": "I believe this work would be of interest to the TMLR community. Dataset condensation and distillation are active research areas in machine learning, with direct implications for data-efficient training, memory efficiency, and the scalability of large models. By focusing on the overlooked issue of color distribution—a non-trivial aspect in both classification and generative settings—the paper contributes a novel perspective that could inspire follow-up research.\n\nSpecifically, researchers in areas such as efficient dataset design, generative modeling, and data augmentation would find value in the proposed diffusion-based color compensation strategy. The demonstration that the method generalizes across architectures and also applies to diffusion generative models broadens its potential audience. Even if some details (e.g., qualitative fidelity of generated images) remain debatable, the methodological direction and empirical findings are relevant to with TMLR’s audience.", "requested_changes": "Major\n\n- The KDE curve in Figure 2(d) is somewhat confusing. The density distribution appears significantly higher than the original data in the marginal bins, but lower in the central bins. While DC3 clearly avoids the uniform distribution issue seen in traditional DC methods, the resulting distribution also seems noticeably different from the original dataset. This raises doubts about the claim that “the KDE curve is closer to the real dataset than other methods.” In addition, Channel B in Figure 2(d) does not seem consistent with the other subfigures. Please clarify or correct me if I have misunderstood.\n\n- Regarding the IPC concept, it is unclear whether the proposed color compensation strategy effectively increases the number of samples per class after diffusion-based enhancement. Clarifying this relationship would help position the method more precisely within the dataset condensation framework.\n\n- The ablation study in Table 8 is also somewhat confusing. Since the focus is on sampling strategies, I would expect to see a more direct comparison, such as DC3 (with clustering) versus DC3 (with random sampling), to better isolate the benefit of clustering.\n\n- For Figure 11, the differences between the first row (fine-tuned with the original data) and the second row (fine-tuned with the mixed data) are not visually obvious. To provide a more complete comparison, it would be helpful to include an additional row showing DiT results without fine-tuning, consistent with the setup in Figure 8.\n\n- Finally, the condensed images shown in Figures 12–15 (from CIFAR-10/100, ImageWoof, and ImageNette) often appear unusual, with images seemingly split into two hues. Is this an expected artifact of the diffusion-based color compensation strategy? If so, further explanation is needed regarding why these results occur and how such images can still benefit generative model training.\n\nMinor\n\n- Switch the positions of Figures 5 and 6 for better flow.", "broader_impact_concerns": "No concerns on the ethical implications.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "uLUTHTxb3K", "reviewer": "Reviewer_ARJN", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper introduces ​​DC3​​, a novel dataset condensation method that addresses two key bottlenecks in existing methods: ​​color homogenization​​ and inefficient compression. Specifically, DC3 first proposes a diffusion-based mechanism to enhance color diversity in condensed images by leveraging hue prompts. Then, DC3 replaces random bin sampling in Dataset Quantization (DQ) with a clustering-guided strategy to preserve critical samples. Experiments demonstrate robustness of proposed method across CNNs and Transformers.", "claims_and_evidence_yn": "Yes", "claims_explanation": "Experiments demonstrate the effectiveness of proposed method.", "audience_yn": "Yes", "audience_explanation": "The proposed method alleviate color homogenization​​ and inefficient compression problems existed in dataset condensation.", "requested_changes": "I suggest authors to conduct experiments on ImageNet to verify the effectiveness of DC3, which will make experiments more convincing.", "broader_impact_concerns": "No ethical concerns.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)