Multimodal Dataset Distillation Made Simple by Prototype-Guided Data Synthesis
Authors: Junhyeok Choi
Sangwoo Mo
Minwoo Chae
Venue: ICLR 2026 Poster (2026)
Keywords: Dataset distillation
Dataset condensation
Vision-language models
Learning-free approach
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: This paper proposes the first learning-free multimodal dataset distillation framework that generalizes across architectures.

Abstract: Recent advances in multimodal learning have achieved remarkable success across diverse vision–language tasks. However, such progress heavily relies on large-scale image–text datasets, making training costly and inefficient. 
Prior efforts in dataset filtering and pruning attempt to mitigate this issue, but still require relatively large subsets to maintain performance and fail under very small subsets.
Dataset distillation offers a promising alternative, yet existing multimodal dataset distillation methods require full-dataset training and joint optimization of image pixels and text features, making them architecture-dependent and limiting cross-architecture generalization.
To overcome this, we propose a learning-free dataset distillation framework that eliminates the need for large-scale training and optimization while enhancing generalization across architectures. 
Our method uses CLIP to extract aligned image–text embeddings, obtains prototypes, and employs an unCLIP decoder to synthesize images, enabling efficient and scalable multimodal dataset distillation.
Extensive experiments demonstrate that our approach consistently outperforms optimization-based dataset distillation and subset selection methods, achieving state-of-the-art cross-architecture generalization.

Published as a conference paper at ICLR 2026
MULTIMODAL DATASET DISTILLATION MADE SIMPLE BY PROTOTYPE-GUDDE DATA SYNTHESIS
Junhyeok Choi, Sangwoo Mo & Minwoo Chae
Department of Industrial and Management Engineering Pohang University of Science and Technology {cjunh4811 sangwoo mo, (ohaae)ppostcch.ae.
ABSTRACT
Recent advances in multimodal learning have achieved remarkable success across diverse vision-language tasks. However, such progress heavily relies on large- scale image-text datasets, making training costly and inefficient. Prior efforts in dataset filtering and pruning attempt to mitigate this issue, but still require relatively large subsets maintain performance and fail under very small sub- sets. Dataset distillation offers a promising alternative, yet existing multimodal dataset distillation methods require full-dataset training and joint optimization of image pixels and text features, making them architecture -eppeede and limiting cross-architecture generalization. To overcome this, we propose learning-free dataset distillation framework that eliminates the need for large-scale training and optimization while enhancing generalization across architectures. Our method uses CLIP to extract aligned image-text embeddings, obtains prototypes, and em- ploys an unCLIP decoder to synthesize images, enabling efficient and scalable multimodal dataset distillation. Extensive experiments demonstrate that our ap- proach consistently outperforms optimization- dataset distillation and sub- set selection methods, achieving state-of-the architecree generalization. Our code is available at https: //t: thub. com/ junhyeok 9712 /PDS.
INTRODUCTION
Multimodal models, such as CLIP (Radford et al., 2021), have emerged central paradigm in ma- chine learning, demonstrating remarkable generalization across diverse tasks, including zero-shot classification and image-text retrieval. At the core of these successes are large-scale image-text datasets, such as LAION-5B (Schuhmann et al., 2022), whose use entails substantial computational and memory costs during training. This challenge naturally raises critical question: Can we sub- stantially reduce the number of training samples without severely compromising performance? Ad- dressing this question not only crucial for resource efficiency but also offers several practical advantages. Compact distilled datasets enable faster training, which facilitates rapid benchmarking of models and training strategies, including hyperparameter tuning and neural architecture search (Zhao et al., 2021). They are also effective in continual learning settings (Zhao & Bilen, 2021) that require quick adaptation to new data or tasks. Moreover, distilling massive datasets into smaller essential forms provides new insights into the core information required for effective learning.
A variety of approaches have been proposed to reduce dataset size, including coreset selection (Fara- hani & Hekmatfar, 2009; Welling, 2009), dataset filtering (Gadre et al., 2023), and pruning meth- ods (Yang et al., 2023; Mahmoud et al., 2024), which have shown competitive performance in the context of CLIP. However, since they rely representative subsets of the original dataset, they are effective only with sufficiently large reduced datasets and fail once the subset is too small to preserve semantic diversity. In contrast, dataset distillation (Wang et al., 2018) synthesizes new samples that encode the semantics of the original dataset, producing compact yet highly informative datasets. This makes particularly effective on extremely reduced datasets, where it has outper- formed selection-bard based strategies in image classification (Zhao & Bilen, 2021; Cazenavette et al., 2022), even with only a few distilled samples per class.
"Corresponding author

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Published as a conference paper at ICLR 2026
Optimization-bann
Model training syntheels
Parameters comples
Modil
nor non-ganeniiiabll
Cradiant Dow
Modell
Manching
Depardeett
Text festum
Learning-fres free
Prototype guide synthests
Architecture generaltzable
Image
Posture oxtruction
Protohype
Putraype
Model
Discovary
Independent
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Figure 1: Concept. Optimization-basee methods are computationally expensive and architecture- dependent. Our learning-free framework simple, efficient, and generalizable across architectures.
However, research on multimodal dataset distillation remains underexplored, despite the increasing importance of multimodal learning. Existing approaches (Wu et al., 2024; Xu et al., 2024) are typ- ically optimization-base based, jointly optimizing image pixels and text features. Unfortunately. these methods face severe computational and scalability challenges. They require repeatedly training models on the full dataset, storing all intermediate parameters, and reusing them during sample syn- thesis. As datasets and model capacities grow, this process becomes prohibitively time-consuming Moreover, the number of parameters to optimize increases linearly with
and memorr-intensiin the number of synthetic samples, and numerous hyperparameters (e.g., learning rates for image (Zhao
and text) must be carefully tuned. More critically, these methods are architecture-depender al., 2023b; Zhong & Liu, 2023). The resulting synthetic dataset is nearly identical the original data, as the distillation process essentially adds architecture-dependent adversarial perturbations to the initialization images (see Figure 3). Consequently, these distilled datasets generalize poorly when applied to different model architectures, requiring the distillation process to be repeated from scratch. This requirement imposes substantial computational cost and severely limits the practical applicability of existing methods.
In this paper, we propose the prototype-guides data synthesis (PDS) framework, the first learning- free multimodal dataset distillation that is both simple and effective. Unlike optimization- bbasse approaches, PDS requires no training or fine-tuning and avoids learning pixels and text features, making it computationally efficient, architecture-iddeenddee
and broadly generalizable (See Figure 1). Recent works have introduced learning-fre methods for image classification (Su et al., 2024; et al.. 2025), where images are generated for each class using explicit class labels. However, these methods cannot be extended to multimodal datasets because they do not consider cross-modal alignment. Aligned image-text pairs essential for multimodal learning (Chen al., 2020; Li et al., 2021). However, the VAE encoder (Kingma & Welling, 2014) commonly used in image-only distillation, produces embeddings that not aligned with text embeddings from separate text encoders. Consequently, naively extending existing learning- free techniques produces synthetic datasets lacking semantic alignment across modalities. This motivates new learning- approach that enforces modal alignment to synthesize semantically aligned image- textee-exttpee pairs.
We use CLIP (Radford et al., 2021) capture image-text aligmment in learning-fre manner. For each modality, we extract features and perform clustering to obtain semantically diverse clusters. We then formulate a linear assignment problem to align these clusters across modalities, obtaining image-tex, text prototypes. These prototypes are then used to synthesize images. However, since stan- dard Stable Diffusion models (Rombach et al., 2022; Podell et al., 2024) cannot generate images conditioned on CLIP image embeddings, we build on the idea of unCLIP (Ramesh et al., 2022; Pa- tel et al., 2024), which enables such conditioning. Our ablation studies validate this design choice by confirming the importance of image prototype-based synthesis. Furthermore, experiments show that PDS generalizes well across architectures, reducing dependence on specific architectures and outperforming optimization-base multimodal dataset distillation methods. We also demonstrate the limitations of subset selection when the reduced dataset is too small, and show that image-only learning- free approaches cannot be extended multimodal settings without cross modal alignment.
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2 RELATED WORK
Dataset distillation. Dataset distillation (Wang et al., 2018) seeks to synthesize a compact synthetic dataset that can replace large-scale dataset while preserving performance close to training on the full dataset. Most prior work has primarily focused on image classification, aiming to synthesize a few representative images per class, with its potential applications in continual learning (Zhao & Bilen, 2021; Zhao et al., 2021; Zhao & Bilen, 2023), privacy-preservee data sharing (Li et al., 2020; Chen et al., 2022; Dong et al., 2022), and neural architecture search (Zhao et al., 2021; Zhao & Bilen, 2023). We first review optimization-bin and then address multimodal dataset distillation.
and generative approaches in image-only settings
Optimization-basee methods. Early work formulates dataset distillation as bi-level optimiza- tion problem, where synthetic data are optimized to match the training dynamics induced by the full dataset. These approaches generally incur substantial computational overhead, limiting their scalability to high-resolution images. Gradient matching (Zhao & Bilen, 2021; Zhao et al., 2021; Lee al., 2022) enforces matching at the gradient level, whereas trajectory matching (Cazenavette et al., 2022; Cui et al., 2023; Du et al., 2023) matches the parameter trajectory during training to capture long-term dynamics. TESLA (Cui al., 2023) makes trajectory matching more practical by proposing a variant that is more efficient in memory and computation time.
To further improve scalability, recent work has proposed several extensions. Decoupled optimiza- tion methods such as SRe*L (Yin et al., 2023) adopt squeeze-recovve--rllaeel procedure. Building on this, subsequent work explores multi-backhone generalization (Shao et al., 2024a), patch-based recovery (Sun al., 2024), which was later extended with refinement strategies (Tran et al., 2025), and adaptive or category-awand optimization (Shao et al., 2024b). A complementary line of research parameterizes the synthetic dataset with compact factorized representations, such shared bases with learnable recombination functions (Liu et al., 2022; Deng & Russakovsky, 2022; Wei et al., 2023) and crature-sharing mechanisms (Zheng et al., 2024; Zhang et al., 2025a). Alternative pa- rameterizations include leqquencydoomale (Shin et al., 2023) and neural-field representations (Shin et al., 2025). In parallel, feature-distribution matching has also been extensively studied (Wang et al., 2022: Liu et al., 2023; Zhao & Bilen, 2023; Zhao et al., 2023a; Zhang al., 2024).
Generative approaches. Beyond pixel-space optimization, generative models have emerged as an alternative paradigm. One line of work optimizes the latent codes of pretrained generators, including GANs (Zhao & Bilen, 2022; Zhang et al., 2023), StyleGAN variants (Cazenavette et al., 2023; Zhong et al., 2025), and latent diffusion models (Moser et al., 2024; Wang et al., 2025). Another line fine- tunes pretrained generators, such as class-conditional GANs (Zhang et al., 2023) or diffusion models (Gu al., 2024; Su al., 2024; Zou al., 2025). While effective, these methods are still computa- tionally expensive due to the large number of trainable parameters and repeated forward-backwart passes. Recent work proposes learning-free approaches that leverage pretrained diffusion models without additional fine-tuning. Specifically, image embeddings are clustered within each class, and representative embeddings from each cluster are used synthesize new images (Su et al., 2024; Chan-Santiago et al., 2025; Zhao et al., 2025). These methods achieve competitive performance while substantially reducing computational cost.
Multimodal dataset distillation. Existing multimodal dataset distillation approaches are optimization-baser while learning-free methods remain unexplored. The first work (Wu et al., 2024) adopted trajectory matching (Cazenavette et al., 2022) via bi-level optimization to jointly learn image pixels and text features. To mitigate the substantial time and memory costs, the TESLA framework (Cui et al., 2023) was later applied, and Xu et al. (2024) further extended this line of work by replacing the InfoNCE loss with a weighted binary cross-entropy loss and introducing a learnable similarity matrix between synthesized images and texts. Subsequent studies (Zhang al., 2025b; Dang et al., 2025) refined these strategies, further advancing the optimization- paradigm.
3 PROTOTYPE- DATA SYNTHESIS
3.1 PROBLEM FORMULATION
Let a large-scale image-text dataset D = ((I,. H be given, where x, denotes an image and Da its paired caption. The objective of multimodal dataset distillation is to synthesize compact yet
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Cluetering
matchinng
Image- Teat Mattch Courts
Matiched Clartera
Protelypes
CLI Image Encoder
ci c2
Image Ned
[ctne [G [tr)
Encodir
STEP 3 image synthesis
[
Diffusion Process
(-
Guidence
IDistiled sample]
Byythetic Image)
Retrival
Figure 2: Our PDS framework distills multimodal datasets by synthesizing samples from image- text prototypes through three stages: (i) modality-speeinf clustering of CLIP embeddings, (ii) cross- modal cluster matching via a linear assignment to obtain prototypes, and (iii) image synthesis using an unCLIP decoder guided by image prototype and caption embeddings.
informative dataset S {(ām. Fm JM m=1 M < N, such that vision- lisoonlanguaage model trained on S achieves performance without significant degradation compared to training on D. This enables efficient model training with reduced computational and storage costs. Formally, for samples B {Z1,, y1 )
from the underlying data distribution P, the objective can be formulated as:
S arg min Es-pes [[ 0s) [(B:0D)] subject 6x arg min L(X:
S
where denotes contrastive loss such as InfoNCE. In practice, since text is inherently discrete, dataset distillation performed in the text embedding space, while images are parameterized in the pixel space. Existing methods typically rely on optimization-based approaches, which require repeated training on the full dataset and storage of intermediate parameters, resulting in significant computational and storage overhead, Moreover, their performance architecture-dependen. often degrading when applied to models with different backbones.
3.2
OUR PDS FRAMEWORK
We propose simple yet effective PDS framework for multimodal dataset distillation that uses pre- trained models without fine-tuning. The core idea of PDS is to identify image-text prototypes and synthesize new images based on these prototypes to construct a compact dataset. shown in Figure 2, the pipeline consists of three steps: (i) clustering the CLIP embeddings of images and texts, (ii) matching the clusters to obtain cross-modal prototypes, and (iii) synthesizing images.
Modality-specinf clustering. We adopt CLIP encoders (Radford et al., 2021) to obtain image and text embeddings that preserve high-level semantics and remain aligned across modalities. This alignment is particularly important for learning-free free dataset distillation, where modality alignment cannot learned. In fact, using an image encoder that is not aligned with a text encoder typically leads to poor performance, as shown by the VAF-based baselines in Table 3, thereby motivating the use of CLIP for both modalities. After extracting image and text embeddings { 2/ ) prune pairs with low similarity scores to remove noisy or weakly aligned pairs, resulting in a refined subset. We then perform clustering separately for each modality, producing clusters {Cim Jm=1 and {Cm) (CuMM m=1 that group semantically similar embeddings and collectively capture the dataset's broad semantic diversity, where M is set the desired number of distilled samples. To handle large datasets efficiently, we adopt the mini-batch k-means algorithm (Hartigan & Wong, 1979; Sculley, 2010) (see Appendix C.I).
Cluster matching for prototype construction. While we obtain semantically coherent clusters within each modality, semantically related image-text clusters not aligned across modalities. To
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establish cross-modal correspondences, measure the association between m and Cj by the number of shared image-text pairs whose embeddings belong to the respective clusters. Clusters with more shared pairs regarded as more semantically related, and we therefore aim to find one-to-one cluster matching that maximizes the total number of shared pairs.
To this end, we formulate the problem framework widely used in logistics and scheduling. Specifically, we construct a cost matrix E RMM where each entry represents the negative of the number of shared image-texi pairs between a given image cluster and a text cluster:
linear assignment problem (Burkard & Cela, 1999),
K-j- (1( Ma)
iing Zmm
E
_KL An E C.
Maximizing the total number of shared pairs is therefore equivalent to minimizing the total cost:
M ΣΣ
M
M Σ P
M
min
Pu =1
subject to
D Pg e (0,1), -1
where P is a permutation matrix indicating the chosen matches. We solve this problem using the Hungarian algorithm (Munkres, 1957; Jonker & Volgenant, 1987), which computes the optimal one- to-one matching between image and text clusters.
For each matched cluster pair (Cime, ). we retain only the embeddings of the shared image-text pairs. Embeddings whose paired embedding does not belong to the matched cluster of the other modality are discarded, as they are unlikely to be semantically related to the features in the matched cluster. This filtering strategy strengthens cross-modal alignment between matched clusters (See
Appendix C.2). We then obtain the prototypes (s. 2.5))) for each matched cluster pair by averaging the retained features. If no shared pairs exist, instead use the original cluster centers to preserve the intended distilled dataset size. Matched clusters without shared pairs (hereafter simply referred to as pairless clusters) rarely appear when the distilled set size is small, and keeping discarding them yields nearly identical performance. However, as the distilled set size increases, these pairless clusters become more frequent and their centroids tend to become misaligned, weakening cross- modal alignment and degrading performance. In these larger-scale regimes, discarding pairless clusters is therefore preferable. Additional discussion provided in Appendix C.3. The resulting text prototypes are used as the distilled text features.
Image synthesis. We aim to synthesize images that encode information captured by image pro- totypes, since selecting real-image subsets does not adequately preserve the dataset's s semantic di- versity, which leads to lower performance as shown in Table 2. To synthesize images efficiently, we use generative model rather than pixel-space optimization, Table 4 demonstrates that di- rect optimization is slow and memory-intensive. However, existing learning-fres dataset distillation methods cannot leverage image prototypes, standard Stable Diffusion models (Rombach et al., 2022: Podell et al., 2024) do not condition their denoising U-Net bRonneberger et al., 2015; Ho et al., 2020) CLIP image embeddings. Similarly, text-to-image generation methods based retrieved captions rely solely on text, leaving image prototypes unused.
To address this challenge, we build the idea of unCLIP (Ramesh al., 2022; Razzhigaev al., 2023; Patel et al., 2024), which adopts a two-stage pipeline: a separately trained prior maps text into the CLIP image embedding space, and a decoder then generates images from these embed- dings. In our approach, we employ only the unCLIP decoder, conditioning directly on CLIP image embeddings. This design enables the model utilize image prototypes for generation, resulting in semantically rich synthesized images, as illustrated in Figure 3. In addition, it improves retrieval performance over baselines that either do not use image prototypes or use them only to select real images, as demonstrated in Table 5. We further strengthen semantic alignment by incorporating text prototype. Because the unCLIP decoder cannot condition on CLIP text embeddings, we instead retrieve the caption most similar to the text prototype from the training set and use it as an additional condition. During diffusion, generation is guided by both the image prototype and the retrieved caption, maintaining semantic alignment. This additional caption condition further improves perfor- mance (Appendix C.4). To the best of our knowledge, this is the first dataset distillation method that generates images directly from CLIP image embeddings using an unCLIP decoder.
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Table 1: PDS distills datasets with superior cross-architecture generalization compared mul- timodal dataset distillation baselines. We evaluate on unseen vision backbones, comparing against
TESLA-VI and LoRS on Flickr30K (top) and MS-COCO (bottom). Across all distilled dataset
sizes and evaluation backbones, PDS consistently outperforms both baselines, demonstrating that its distilled datasets generalize effectively to unseen backbones.
Evaluation Model
Pairs
Methods
IR@1
IR@5
IR@ 10
TR@1
TR@5
TR 10
TESLA-VL
4.1 2.3
14.7 0.9
22.9
6,5 0.4
17.8 1.4
27.3 = 1.4
100
LoRS
6.3
18.6
28.0
9.1 +02
24.3
34.5 0.8
PDS
0.9
25.8
0.3
10.2 0.3
2.9
39.0
ResNet
TESLA-VL
4..
28.8 0.6
40.6
14.9
1.
48.8 8
300
LoRS
8.6
23.5
33.5
14.7 1.2
33.5 +.8
44.1 +32
PDS
1..4
38.1 0.2
51.4
18.7 0.5
45.0 0.4
57.8 0.6
Full Datasct
59.6 0.1
71.4
46.0 0.6
76.2 + 0.3
84.4 +
TESLA-VL
2.3
7.8 2.7
1
2.6
8.9
13.7
100
LoRS
2.8+0.1
9.9
16.1
5.2
13.6
20.5
PDS
2.3
28.5 0.4
6.6 0.5
4.5
26.9 = 0.5
VIT
TESLA-VL
5.1
1.02
24.5
6.2 6.66
18.0
27.3
300
LoRS
4.1
1.0
20.7
17.1
25.7
PDS
9.1
2.4
3.4
9.6 0.3
2.5
37.5
Full Dataset
22.7 0.3
49.9 0.3
62.3 0.3
35.5 0.4
62.9 + 0.3
73.0 * 0.4
TESLA-VL
1.1
0.2
10.2 0.
+.33
12.5 0.3
100
LoRS
1.8
6.8 =.2
11.4 0.4
2.3 +02
0.5
12.6
PDS
2.1
10.0 = 0.2
17.3 0.3
4.5 2.2
14.0 0.3
21.4 = 0.4
ResNet
TESLA-VL
3.0
10.7
17.6
5.7
17.0
25.6
300
LoRS
2.5
8.5 0.4
13.8 006
3.2
10.1 0.4
15.9
PDS
5.3
17.2 = 0.4
27.2 0.6
7.4 0.3
20.7 0.3
30.2 =
Full Dataset
12.6
3..4
46.5.000
26.6
53.1 0.2
66.1 +.1
TESLA-VL
0,5
208
3.8 1.4
0.1
0.5
1.7 0.9
100
LoRS
0.8
3.0
5.4
0.2
2.4 0.3
4.1 A.1=00 D.5
PDS
2.1
8.6 0.1
14,5 0.1
2.11
7.7 2.22
13.2 0.3
VIT
TESLA-VL
1.5
5.9
10.1
0.2
7.2
0.4
300
LoRS
1,0
1.7
6.3
1.3 0.4
4.6
7.4
PDS
4.1
1..4
21.2
3.7 0.11
12.6
19,8 + 0.2
Full Dataset
11.5
29.8
41.7
19.5
42.4
55.3
EXPERIMENTS
This section presents extensive empirical results that validate the effectiveness of PDS. Perfor- mance comparisons highlight its strengths in cross-generalization. effectiveness on extremely re- duced datasets, and suitability for multimodal datasets. In addition, the ablation studies support design choices, including the use of generative models and prototype-based image synthesis. In all tables, the best results highlighted in bold, and the second-best are underlined.
4.1 EXPERIMENTAL SETTINGS
Datasets and metrics. We evaluate our approach on two widely used vision-language benchmarks: Flickr30K (Plummer et al., 2015) (29,000/1,00) 1,0000000 images for train/test) and MS-COCO (Lin et al., 2014) 1113,000550000 images for train/test), where each image is paired with five human-annotated captions. Model performance is evaluated using recall at & @ k) for cross-modal retrieval, with E (1, 10). For each query from one modality, we compute cosine similarity with all candidates from the other modality in the test retrieve the top-k most similar samples, and check whether the ground ground-truth match is included among them. We report text-to-image retrieval as IR@ & and image-to-text retrieval as TR @...

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Implementation details. Unless otherwise specified, all baselines use CLIP VIT-L/14 (Radford et al., 2021). For PDS, images are generated with the unCLIP decoder using classifier-fre free guidance (Ho & Salimans, 2022) with a guidance scale of 5.0 and 100 sampling steps, and resized to 224 x 224 for evaluation. To assess cross-arce coass-rchhitectre generalization, we follow Xu et al. (2024) and evaluate whether the distilled datasets remain effective when transferred alternative vision backbones such as ResNet-50 (He et al., 2016) and ViT-Ti/16 (Dosovitskiy et al., 2021). For this evaluation, we use each alternative backbone as the image encoder and attach learnable linear projection layer after the text encoder. We then fine-tune the CLIP-style model on the distilled dataset while keeping the text encoder frozen. For all baselines, we adopt the hyperparameters reported in the original papers, and conduct all experiments on single RTX 3090 GPU.
4.2 PERFORMANCE COMPARISON
We evaluate PDS against three categories of baselines: multimodal dataset distillation methods, dataset subset selection methods, and learning-free distillation methods for image classification. These comparisons are designed highlight three aspects of our contribution: (i) mitigating the cross-generane gonssseeneralizttion limitations of existing multimodal dataset distillation methods, (ii) demonstrat- ing the advantage of PDS over subset selection when the reduced dataset extremely small, and (iii) overcoming the limitations of existing learning-free distillation approaches when extending from image classification to multimodal datasets. Detailed descriptions of each baseline are provided in Appendix A.
Multimodal dataset distillation. A key limitation of architecture-deeendett distilled datasets is that they require re-distillation whenever new backbone is introduced. To examine whether the distilled dataset can generalize to unseen backbones, we compare PDS against multimodal dataset distillation baselines, TESLA-VL and LoRS (Xu et al., 2024), in cross arosstacchieeeture setting. We exclude MTT-VL (Wu et al., 2024) due to its higher computational and memory cost as well as its lower performance compared to these methods. The baselines use pretrained NFNet (Brock et al., 2021) as image encoder, following Xu et al. (2024), while we replace the original BERT text encoder (Devlin et al., 2019) with the CLIP text encoder for fair comparison. This replacement also improves performance (see Appendix C.5).
As shown in Table 1, PDS consistently outperforms both baselines Flickr30K (Plummer al., 2015) and MS-COCS (Lin et al., 2014) across all distilled pair sizes and evaluation backbones. For example, with 300 distilled pairs and a ResNet backbone, PDS achieves 14.4% IR@1 and 18.7% TR@1 on Flickr30K, surpassing baselines over 4. and 3.8 percentage points (pp), respectively. The performance gap increases higher retrieval cutoffs (e.g..+ +10.8 pp in IR 10). These results reveal a key limitation of optimization-ben based baselines. Their distilled datasets are strongly architecture- dependent and require expensive re-distillation for each new backbone. In contrast, PDS produces distilled datasets that generalize well across architectures through a simple and efficient distillation process, enabling broad practical applicability.
Dataset subset selection. To obtain a highly reduced yet informative dataset, representative images must preserve broad semantic coverage. In this case, dataset distillation offers more effective so- lution than subset selection, as it synthesizes data to preserve diverse semantic features. To validate this, we evaluate PDS against classical coresct-selcction methods, K-center (Farahani & Hekmatfar, 2009) and Herding (Welling, 2009), as well widely used filtering schemes such as CLIP score filtering, LAION filtering, and image-based filtering (Gadre et al., 2023).
As shown in Table 2, PDS consistently achieves the highest performance, outperforming the strongest baseline (Herding) by 17.2 and 10.8 pp in IR@ 10 and TR @ 10, respectively. Although dataset filtering methods widely used in CLIP training due their effectiveness with moderately reduced datasets, they fail with extremely reduced datasets. These results demonstrate that PDS is particularly well-suited for such scenarios, it preserves broader semantic coverage by interpolat- ing diverse semantic features and achieves superior performance.
Learning-free approaches for image classification. We examine whether learning-free dataset distillation methods for image classification can be extended to the multimodal setting. To inves- tigate this, we extend D (Su et al., 2024) and MGD (Chan-Santiago et al., 2025) by retaining their Stable Diffusion VAE (Rombach et al., 2022) the image encoder and additionally incorpo-
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The representative samples obtained by interpolating diverse semantic features are more suitable than subsets of the original dataset when the reduced dataset is extremely small. We compare PDS with subset selection methods in the 100-pair setting on Flickr30K. Baselines re- stricted to real samples are limited in preserving broad semantic diversity. In contrast, PDS synthe- sizes representative samples that reflect diverse semantics and consistently achieves superior results.
Table 2:
Subset selection
Distillation
Evaluation Model
Metric
CLIP score
LAION filtering
Image- based
cecenter
Herding
PDS
IR@1
2.9 2.1
3.6 #.2
2.5 0.1
2.4
2.2 0.2
7.9 = 0.3
IR s
10.4 0.3
12.6 00.5
8.3
8.5 4.2
8.6
25,8 0.4
IR @ 10
16.8 2.3
20.1 1066
14.5
24.
13
37.3
ResNet
TR
0.3
6.7
4.7
4.7
4.0
10.2 +.3
TR
15.6
1.74
12.6
+..4
10.5
28.2 =
TR @ 10
24.1 004
28.2 #0.
18.9 0.7
19.3
16.2
39.0 = 0.3
Table 3: Cross-modal alignment is crucial for effective multimodal dataset distillation. We com- pare PDS with learning-free methods image classification in the 100-pair setting on Flickr30K. Both D'M and MGD² rely on the VAE encoder, which produces image features that are not aligned with text features, leading to low retrieval accuracy. In contrast, PDS uses CLIP encoders to enforce cross-modal alignment, outperforming the baselines and highlighting the importance of alignment- aware distillation.
Evaluation Model
Methods
IR@1
IR@5
IR@10
TR@1
TR@5
TR@10
D'M
1.3 a.1
5.8 0.2
9.8
1.4 2.2
5.6
9.5 U.3
ResNet
MGD²
2.6
10.0
17,2
3.4
11.6
17.5 204
PDS
7.9 0.3
25.8 0.4
$7.3+00
10.2 0.3
28.2 0.9
39.0 0.3
rating CLIP text encoder. As in our method, prototypes are obtained through clustering and cluster matching, after which each baseline applies its original procedure to synthesize images.
Although these methods perform competitively on image classification, their reliance on the VAE encoder leads poor alignment with CLIP text embeddings and consequently degrades retrieval performance (Table 3). For example, with ResNet, PDS achieves 37.3% IR @ 10 and 39.0% TR @ 10, whereas MGD reaches only 17.2% and 17.5%. These findings highlight the importance of strong cross-modal alignment for effective multimodal dataset distillation and demonstrate the limitations of existing learning-fir freee methods in multimodal settings.
4.3 ABLATION STUDIES
Advantage of using generative model. We analyze the benefits of using generative model by comparing PDS with optimization-based multimodal dataset distillation methods. Such optimization-based approaches often synthesize images nearly identical the initialization sam- ple (Figure 3, left), essentially adding architecture architectureddepeddett adversarial perturbations rather than generating genuinely new data, thereby limiting semantic diversity. contrast, PDS employs a gen- erative model to synthesize images from prototypes constructed by interpolating diverse semantic features.
PDS further improves efficiency by eliminating pixel-space optimization. To validate this, we com- pare PDS with CLIP inversion (Kazemi et al., 2024), which directly optimizes pixel values so that the CLIP embedding of synthesized image aligns with either an image or text prototype. We eval- uate two variants: (i) Image prototype alignment and (ii) Text prototype alignment. As shown in Table PDS reduces peak memory usage (from 6.13 GB to 4.34 GB) and dramatically shortens the generation time (from 1,477 to 9.7 s per image). addition, CLIP inversion produces unrealistic images (Figure 3, middle), resulting in poor performance (Table
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black dog running Into the
paddles down rapids kayak
group of people are gethered large outdoor
Initial sample
TESLA VL
LoRS
CLIP Inversion
Retrieval
UnCLIP
PDS
The synthesized images. Left (col. 1-3): Images from multimodal dataset distillation baselines, which nearly identical to the initialization image. Middle: Image generated via CLIP inversion from image prototypes, which not realistic. Right (col. 5-7): Given a text prototype, we first retrieve the most similar caption and present three images: the paired real image, the UnCLIP- generated image from this caption, and the PDS-generated image. Unlike UnCLIP, which strictly follows captions, PDS produces realistic and semantically enriched images by conditioning on image prototypes.
Figure 3:
Table 4: Using generative model for image synthesis improves both efficiency and perfor- mance. We evaluate two variants of CLIP inversion: (i) Image prototype alignment and (ii) Text pro- totype alignment, both which directly optimize pixel values, in the 100-pair setting on Flickr30K. By employing generative model, PDS accelerates image generation, reduces memory usage, and produces realistic images, achieving superior performance over CLIP inversion.
Evaluation Model
Methods
IR@1
TR@1
Memory (GB)
Time (s)
Text alignment
4.1
2.0 u.5
6.13
1477.71
ResNet
Image alignment
4.2
4.2 +.3
PDS
10.2 7.3
4.34
9.71
Table 5: Incorporating an image prototype through the unCLIP decoder improves perfor- mance. We compare PDS with two retrieval baselines and an unCLIP-based text-to-image genera- tion method in the 100-pair setting on Flickr30K. While the retrieval baselines rely on real images and unCLIP generates images from text alone, PDS incorporates image prototypes and textual formation through the unCLIP decoder, leading to superior performance.
Evaluation
Methods
IR @ 1
IR@5
IR@ 10
TR@1
TR@5
TR 10
Model
Text-prototype
5.220.1
17.5a0.2
27.
6.410.2
19.4+0.4
28.240.6
Image-proe eaoee-protttye
5,520.3
19.020.1
28. 28.7
8.010.4
20.4
30.2+0.6
ResNet
unCLIP
5.220.2
17.1
26.720.3
6.420.5
19.520.8
28.940.
PDS
7.9+0.3
25.8+0.4
37.3+0.3
10.240.3
28.2+n.9
39.0+0.3
Impact of image prototype-based image generation. To assess the impact of image prototype- based generation, we compare PDS with three baselines that obtain images through different mecha- nisms. First, we consider two baselines that do not use image prototypes. The first is text prototype- based retrieval, which retrieves the caption in the training set whose embedding has the highest cosine similarity to the given text prototype and then selects its paired real image. The second is an MICLIP-based text-to-image generation method (Ramesh et al., 2022), which applies the full un- CLIP pipeline (prior and decoder) to generate an image from the retrieved caption. In addition, consider image prototype-basee retrieval, which uses the image prototype only to retrieve the most similar real image from the training set without using it for generation.

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Figure 3 (right) illustrates the benefits of incorporating image prototype into image generation. PDS generates images with richer visual details, producing backgrounds that not only align with the caption but also reflect the interpolated semantic information encoded in the image prototype, result- ing in scenes contextually consistent with both. Quantitatively, PDS consistently outperforms base- lines that do not use image prototypes (Table 5). For example, with ResNet, IR @ 10 improves from 26.7% (unCLIP) and 27.1% % (Text-prototype) to 37.3%. with similar gains observed for TR @ 10. The performance remains substantially below that of PDS even when using the image prototype to retrieve real images Amaage-pooooyype for image generation is crucial for producing compact yet highly informative synthetic samples and achieving strong retrieval performance beyond what can be obtained from captions alone or from retrieving real images.
These results demonstrate that using an image prototype
Additional ablation studies and extended analyses are provided in Appendix C, including compre- hensive analyses of PDS-related hyperparameters, distilled dataset sizes, and alternative decoder conditioning strategies. This appendix also examines robustness cralization to different CLIP variant, and reports results on an additional dataset. Appendix D presents two applications of our method, demonstrating how the distilled dataset can be used within ASIF (Norelli et al., 2023) and how PDS can be incorporated into an optimization-bet based distillation framework.
rare samples, evaluates gen-
5 CONCLUSION
Summary. In this work, we introduce PDS, learning-frre approach to multimodal dataset distilla- tion that considers image-tex: alignment and synthesizes images using image- text prototypes. PDS scalable and efficient, achieving strong cross- roos-generalization and maintaining significant advan- tages even with extremely reduced dataset. This work offers new perspective multimodal dataset distillation and demonstrates a more effective and generalizable strategy.
Limitations. PDS leverages a pre-trained generative model capable of synthesizing images condi- tioned on embeddings. Although stronger models such as SigLIP (Zhai et al., 2023) provide better image-text alignment, the absence of generative models that can be conditioned dings prevents their direct use for learning-free dataset distillation, suggesting a promising direction for future work. Additionally, the prototypes may be more influenced by dominant classes corre- sponding to frequently occurring concepts, while rare or long-tal tail classes may be underrepresented. However, this limitation is shared by subset selection and other distillation baselines, and PDS shows stronger robustness on rare samples than these methods (see Appendix C.9). A separate challenge arises when distilling datasets from domains that differ significantly from those used train CLIPP and the unCLIP decoder. Since these models poorly in specialized areas such as medical imaging, and image generation conditioned on such embeddings may fail. In such cases, both CLIP and the unCLIP decoder need to be fine-tuned.
these embed-
trained primarily natural images, they perform
ACKNOWLEDGMENTS
Funding. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2023-002408611, a Korea Institute for Advance- ment of Technology (KIAT) grant funded by the Korea government (MOTIE) (RS-2024-00409092. 2024 HRD Program for Industrial Innovation), Institute of Information Communications Technol- ogy Planning & Evaluation (IITP)-Global Data-X Leader HRD program grant funded by the Korea government (MSIT) HUTP-2024-RS 044-2200-004412444 and Korea Planning & Evaluation Institute of Industrial Technology (KEIT) funded by the Ministry of Trade, Industry and Resources (No. 2552005-25458052, Development of Core Technologies for Manufacturing Foundation Models)
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LLM USAGE STATEMENT
We employed large language models solely for polishing the writing and checking grammar.
A BASELINES
Dataset subset selection
Herding (Welling, 2009). This method selects samples so that the subset mean approximates the full-dataset mean in the feature space. Starting from an empty set, it iteratively adds the image-text pair whose inclusion minimizes distance between the updated subset mean and the full- dull-dataset mean.
K-center (Farahani & Hekmatfar, 2009). This method selects samples to maximize dataset coverage by choosing samples that are maximally separated. At each step, selects the sample whose minimum distance to the selected set is maximized.
CLIP score filtering. This method selects image-text pairs whose CLIP- based cosine similarity exceeds a predefined threshold. In our experiments, we retain the top-k pairs from the candidate set.
LAION filtering This method follows the LAION-2B filtering scheme in two steps. It first applies cld3 English filtering, retaining pairs whose text is classified as English with probability above a predefined threshold. It then applies CLIP score filtering to select pairs with high CLIP score.
Image-based filtering. This method constructs subset aligned with the ImageNet classes. first applies fast'Text-based English filtering and caption-length filtering. CLIP image embed- dings are extracted from the remaining dataset and clustered using FAISS (Johnson al., 2019). CLIP embeddings of ImageNet images are then assigned to these clusters, and only clusters con- taining least one ImageNet embedding are retained. Samples in these retained clusters form the subset, which is further refined using CLIP score filtering.
Multimodal dataset distillation
TESLA-VL (Xu al., 2024). This method applies the TESLA framework (Cui al., 2023) to reduce the computational and memory costs of trajectory matching in the original work (Wu al., 2024), and further replaces the InfoNCE loss with a weighted binary cross-entropy loss.
LoRS (Xu al., 2024). This method extends TESLA-VL by learning an additional similarity matrix between synthesized images and texts.
Learning-free dataset distillation for image classification
D'M (Su al., 2024). This method leverages latent diffusion model (LDM) and introduces an initialization strategy based on cluster prototypes. Features are extracted from the dataset using a VAE encoder, and k-means clustering is performed for each class obtain specifcc prototypes. These prototypes generate noise through the forward diffusion process, and the resulting noise is used to initialize the reverse diffusion process.
MGD (Chan-Santiago et al., 2025). This approach follows a strategy similar to D Features extracted with VAE encoder, and k-means clustering is applied within each class obtain prototypes. These prototypes then serve as explicit guidance during the reverse diffusion process.
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B ALGORITHM
Algorithm PDS framework
Require: Original real dataset D: CLIP image ans text encoder; unCLIP decoder.
1: > Modality-Speciffee Clustering
2: Extract the CLIP image and text embeddings (a 22x))
3: Perform clustering separately the image and text embeddings obtain Cime and CR
4: D Cluster Matching for Prototype Construction
5: Solve the linear assignment problem:
M ΣΣ
M 1.
Ki subject
=1 P.₃ E 13.
j=1
where Kij denotes the negative of the number of shared pairs between cluster and j.
6: For each matched cluster pair, obtain the prototypes (=!me, ).
7: D Image Synthesis
8: Synthesize images from the prototypes using the unCLIP decoder. Synthetic dataset S
Ensure:
C ADDITIONAL ABLATION STUDIES
C.I EFFECT OF THE CLUSTERING ALGORITIM
We investigate how the choice of clustering algorithm affects overall performance by applying Gaus- sian mixture models (GMM; (Dempster et al., 1977)), agglomerative clustering (Ward Jr, 1963), and mini-batch k-means (Sculley, 2010). As shown in Table 6, PDS achieves robust performance across different clustering methods, indicating low sensitivity to the choice clustering algorithm. Although GMM and agglomerative clustering achieve slightly better performance, their standard implementations require full-batch processing without mini-batch support, making them computa- tionally prohibitive for large-scale data (see Table 7, measured single-modality clustering). In contrast, mini-batch k-means scales efficiently and remains practical for large datasets, making it a suitable choice for real-world scenarios.
Table 6: Comparison of clustering methods. We evaluate PDS in the 100-pair setting on Flickr30K using different clustering algorithms. PDS achieves robust performance, indicating low sensitivity to the choice of clustering algorithm.
Evaluation Model
Methods
IR 1
IR@5
IR@ 10
TR@
TR@5
TR@I 10
GMM
8.3+0.1
26.2+0.3
37.4+0.2
10.9+0.4
41.1+06
ResNet
Agglomerative
9.3a0.2
28.940.3
41.4.0.1
11.620.6
30.2+0.3
41.620.6
Mini-batch k-means
7.940.3
25.8
37.3+0.3
10.2+0.3
28.2+09
39.0e0.3
GMM
6.020.4
19.2+0.3
28.3+0.1
6.004.4
18.2+09
26.9+0.7
VIT
Agglomerative
6.3+0.2
19, 29.9
29.5
6.4+0.5
18,5+0.3
27.4
Mini-batch k-means
6.840.3
19.240.3
28.5+0.4
6.6x0.5
17.5+0.5
26.9+0.5
Table 7: Clustering time comparison. Clustering time (in seconds) across different methods.
GMM
Agglomerative
Mini-batch k-means
Clustering Time (s)
1,469.7
4,387.9
9.0
17

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Published as a conference paper at ICLR 2026
C.2 EFFECT OF THE FILTERING STRATEGY
When obtaining image-text prototypes from the matched cluster pairs, we use only the embeddings of the shared image-text pairs. In this section, we compare this filtering strategy against baseline that uses all embeddings without exclusion. Figure presents histograms of cosine similarity be- tween paired image and text prototypes under both settings. Filtering results in higher similarity, indicating stronger cross-modal alignment and ultimately leading to performance gains, as shown Table 8. Applying the filtering strategy significantly improves performance over the non-filtering baseline. For example, with ResNet, IR @ 10 improves from 28.1 % to 37.3% and TR @ 10 from 30.6% to 39.0%.
Filtering
14
Non-fittering
12
10-
Frequency
0.15
0.20
0.25
0.30
0.35
Similarity Value
Figure 4: Histograms of cosine similarity between paired image and text prototypes in the 100-pair setting on Flickr30K, shown with and without filtering. Filtering leads to higher similarity.
Table 8: Filtering enhances cross-modal alignment. We compare our filtering strategy against non-filtering baseline in the 100-pair setting on Flickr30K. Prototypes obtained from embeddings of the shared image text pairs consistently improve performance.
Evaluation Model
Methods
IR@1
IR@5
IR@
TR@1
TR@5
TR@10
Non-filtering
5.3 0.1
1.14
28.1 +03
7.4 0.5
21.2
30.6 2.5
ResNet
Filtering
7.9 0.3
25.8 0.4
37.3
10.2
28.2
39.0 +
Non-filee Non-filtrring
0.0
0.0
21.3 0.0
2.0
12.4
19.6 2.0
ViT
Filtering
6.8 0.3
19.2 0.3
28.5 1..50044 0.4
6.6
17.5 0.5
26.9 0.5
18

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Published as a conference paper at ICLR 2026
C.3 EFFECTS PAIRLESS CLUSTERS IN BARGE-SCALLE DISTILLED DATASET
To more comprchensively evaluate the effect of matched clusters without shared pairs (pairless clus- ters), the two strategics (keeping all clusters or discarding the pairless clusters) are applied across a wide range of distilled dataset sizes. In the small-scale settings, both approaches show nearly iden- tical performance because only a few pairless clusters arise. However, as the distilled size increases, the number of such clusters grows substantially, and the pairless clusters tend to have misaligned centroids (see Table 9 and Figure 5). This misalignment weakens cross-modal alignment and leads the performance degradation reported in Table 10. At larger distilled sizes, discarding pairless clusters is therefore preferable.
Table 9: Larger distilled sizes yield more pairless clusters. We distill Flickr30K into various numbers of pairs. As the distilled size increases, the number of pairless clusters grows substantially.
Pairs
100
300
500
1000
1500
Number of pairless clusters
8
24
87
166
160
140
Pairless clusters
120
Frequency
100
40
20-
0.00
0.05
0.10
0.15
0.35
0.40
Similarity Value
Figure 5: Histograms of cosine similarity between paired image and text prototypes in the 1000- pair setting on Flickr30K, shown for pairless clusters and clusters with shared pairs. Pairless clusters exhibit lower similarity.
Table 10: Discarding pairless clusters becomes increasingly beneficial at larger distilled sizes. We distill Flickr30K wide range distilled dataset sizes and compare two strategies, keeping all clusters discarding the pairless clusters. While both strategies perform similarly in small-scale settings, discarding pairless clusters becomes preferable when the distilled size becomes large.
Evaluation Model
Pairs
Methods
IR@ @1
IR@5
IR@10
TR@1
TR@5
TR@10
Keeping
7.9 0.3
25.8 0.4
37.3 0.3
10,2 0,3
28.2 + 0.9
39,0 + 0.3
100
Discarding
7.8 4.2
25.9 = 0.4
37.5 0.2
10.0 00
27.5 + 1.2
38.8 409
Keeping
14.4=04 Discarding
18.7 5..4
45,0 +
57.8
300
14.9 0.2
0.5
0.3
19.3
45.0 =
57.2 0.6
ResNet
Keeping
16.3 0.2
41.3 a
54.9
20.
49.3 1.8
61.4 1.44
500
Discarding
17.1
43.0
56.4 +
23.9
50.5
62.0
Keeping
18.2 2..2000
43.5 a 0.2
56.4 a 0.5
24.3 2.4
$2.7 0.6
64.7 4.66
1000
Discarding
19.5 +
46.4 0.4
58.9 +
27.4
55.3 a
67.5 0.6
Keeping
18.9 =
45.6 + 0.2
59.4 19.44...3
26.5 0.3
55,5 + 0.8
67.8 + 1.2
1500
Discarding
20.5 0.2
48.7 + 0.3
62.5 + 0.2
28.8 + 0.5
59.2 + 0.8
71.2 + 0.4
19

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Published as a conference paper at ICLR 2026
C.4 EFFECT OF INCORPORATING CAPTIONS RETRIEVED FROM THE TEXT PROTOTYPE
We generate images conditioned not only on the image prototype but also on a caption retrieved from the text prototype, enhance semantic alignment between the generated image and the text prototype. To evaluate the benefit of this design, we compare our method with a baseline that generates images conditioned solely on the image prototype. As shown in Table 11, incorporating the caption improves performance.
Table 11: Joint conditioning improves performance. We compare our method with a baseline that generates images conditioned solely on the image prototype in the 100-pair setting on Flickr30K. Conditioning on both the image prototype and caption retrieved from the text prototype improves performance.
Evaluation Model
Methods
IR@1
IR@5
IR@ 10
TR@1
TR@5
TR@10 10
Image prototype
6.0 0.3
19.2 0.3
28.3 +.0.1
6.1 0.4
18.2 0
26.9 +
ResNet
Both prototypes
7.9 0.3
25.8 0.4
37.3
10.2 =
28.2 0.9
39.0 0.3
Image prototype
5.4
17.5 +.2
26.5 +.5.5
5.4 0.3
17.8 0.5
25.7
VIT
Both prototypes
6.8
19.2 0.3
28.5
6.6 0.5
17.5 2.5
26.9
C.5 PERFORMANCE COMPARISON BETWEEN BERT CLIP TEXT ENCODER
To ensure a fair comparison of PDS with optimization- based multimodal dataset distillation base- lines, we replace the BERT encoder used in prior work with the CLIP text encoder. As shown in Table 12, this replacement not only ensures fair comparison but also consistently improves perfor- mance.
Table 12: Comparison of BERT and CLIP as text encoders in the 100-pair setting on Flickr30K. Within each method, we compare BERT and CLIP as text encoders under the same vision backbone (ResNet or VIT). In all cases, replacing BERT with CLIP consistently improves performance.
Evaluation Model
Method
IR@1
IR@5
IR@10 10
TR@1
TR@5
TR@N 10
ReST
0.1
1.9 0.2
3.4 +.2
2.0 0.4
7.9 0.5
12.2 +.5.
ResNet+CLIP
4.1 # 0.3
14.7 0.9
22.9 1.2
6.5
17.8 1.4
27.3
TESLA-VI.
ViT+BERT
0,2
1.0 0.2
1.9 0.5
0.5 0.2
2.0.0..
3.9 1.4
ViT+CLIP
2.1 1.33
7.8 0.7
13.1 1.2
2.6
8.7.09
13.7
0.1 ResNet+CLIP
ResNet+BERT
7.3 0.3
11.8 0.2
3.7 0.4
13.4 0.5
19.8 4.0.8
6.3 1.1
18.6
28.0
9.1 0.2
24.3 0.4
34.5
LoRS
ViT+BERT
0.1
2.7 0.1
5.1
1.7 0.2
6.2 0.5
10.3 4.5.
ViT+CLIP
2.11
+.9
16.1
5.2
13.6 0.5
20.5
20

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Published as a conference paper at ICLR 2026
C.6 SENSITIVITY ANALYSIS OF THE HYPERPARAMETERS
We conduct extensive ablation studies on decoder hyperparameters such as the guidance scale and the number of sampling steps, as well as on the PDS-specific hyperparameters, including the clus- tering seeds and the similarity-pruning threshold ratio. We first examine the sensitivity to the de- coder hyperparameters. Varying the guidance scale E {3, 5, 7, 10, 15) and the number of sampling steps E {25, 50, 100, 150, 200} yields consistent retrieval accuracy, with only slight degradation at smaller values where the effect of prototype conditioning weakens. These trends shown in Fig- ure 6, which reports the aggregated retrieval accuracy. computed as the average of IR@ IR@5, IR @ 10, TR @ 1, TR 55 and TR @ 10, using distilled sets of 100 and 300 pairs.
We next analyze the sensitivity to the PDS-specific hyperparameters. Performance remains consis- tent across different clustering seeds, illustrated by the tight box plot shown in Figure 6, using 20 different seeds. The same figure also presents the effect of the similarity-prunine threshold ratio {0.0, 0.1, 0.2, 0.3, 0.5}. showing that moderate pruning reliably improves performance by remov- ing weakly aligned pairs.
M-100
H-100
1--10
Mo000
M-330
Agg
022
Gurdence
Sampling KIMO
mallo
Senieead meire
Figure 6: Ablation study on hyperparameters. The first three panels show the results obtained by varying the guidance scale, the number of sampling steps, and the pruning threshold ratio, spectively, Each panel reports the aggregated retrieval accuracy, computed as the average of IR I, IR @, @ 10, TR@ 1, TR@5, and TR 10, using distilled sets of 100 and 300 pairs on Flickr30K. The final panel presents a boxplot of retrieval accuracy across 20 different clustering seeds. All results in the figure were evaluated using ResNet backbone.
C.7 EFFECT OF THE DISTILLED DATASET SIZE ON PERFORMANCE
Table 13 presents monotonic improvement in performance the distilled dataset size increases, with the results gradually approaching those obtained from the full dataset. In practice, the distilled dataset size may be chosen heuristically based the structure of the data embeddings. When the embeddings form well-separated and compact clusters, a relatively small number of pairs is often sufficient because each cluster generally corresponds to coherent semantic group. In contrast, when the embedding space is more diffuse or spread out, using larger number of pairs is usually beneficial, as it allows the distilled dataset capture more local structure.
Table 13: Increasing the distilled dataset size consistently improves performance. We distill Flickr30K using a wide range distilled dataset sizes and evaluate the resulting distilled datasets. In all cases, larger distilled sets lead to better performance.
Evaluation Model
Pairs
IR@1
IR@5
IR@
TR@1
TR@5
TR
100
7.9 0.3
25.8
+0.
10.2 +03
28.2
39.0
300
1..4
38.
51.4
18.7
45.0
57.8 1.6
500
17.1
43.0
56.4
23.9
50.5
62.0
ResNet
1000
19.5
46.4
58.9
27.4
55.3
67.5 1.6
1500
20.5
4..
62.5
28.8
59.2
71.2
Full Dataset
28.5
59,6
7..1
0.6
76.2
84.4 1.2
21

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Published as a conference paper at ICLR 2026
C.8 ALTERNATIVE CLIP VARIANT
There are two publicly available pre-trained unCLIP models based on CLIP ViT-L/14 and VIT-H/14, respectively. For our main experiments, adopt CLIP ViT-L/14 because it is computationally efficient for extracting image and text features. We additionally evaluate PDS using CLIP ViT-H/14 examine whether the improvement generalizes different CLIP variant. As shown in Table 14, using CLIP ViT-H/I4 14 results in improved performance, likely due to its stronger alignment between image and text features. Importantly, our PDS framework continues outperform all other baselines even with this CLIP variant, indicating that the observed gains are not specific to CLIP ViT-L/14.
Table 14: Results with an alternative CLIP variant in the 300-pair setting on Flickr30K. We evaluate PDS using CLIP ViT-H/14 to examine whether the improvements generalize across a dif- ferent variant. CLIP ViT-H/14 achieves higher performance, likely due to its stronger image-text alignment.
Evaluation Model
CLIP variant
IR@]
IR@5
IR @ 10
TR@1 1
TR@5
TR@ 10
ViT-L/14
14.4 0.4
38.1 + 0.2
51.4 0.4
18.7 0.5
45.0 + 0.4
57.8 0.6
ResNet
VIT-H/14
17.2 0.5
42.6
55.6
23.7
47.3
60.0 0.8
VYT-L/14
9.1
27.3
38.4 0.4
9.6 0.3
26.1 0
37.5 1.2
VIT
VIT-H/14
12.0 0.1
43.2 0.6
12.7 0.6
32.4
44.3 = 0.2
C.9 ROBUSTNESS OF THE PDS TO RARE SAMPLES
The prototypes from PDS may be more influenced by features from dominant classes, which could lead underrepresentation of or long-tail classes. However, is shared by subset selection and other distillation baselines, and PDS empirically demonstrates stronger robustness in such cases. For this evaluation, define rare samples the 200 Flickr30K test samples farthest from the mean embedding of the training dataset. Using the 100 samples se- lected distilled from Flickr30K by each method, we measure the retrieval accuracy on this rare subset. As shown in Table 15, PDS consistently outperforms both subset selection and other distil- lation baselines. These results indicate that, despite the inherent limitation, PDS provides better coverage of rare examples compared to all other baselines.
that this limitation
Table 15: PDS is more robust to rare samples than existing methods. We evaluate each method on 200 rare test samples from Flickr30K, defined as those farthest from the mean embedding of training dataset. In the 100-pair setting Flickr30K, PDS consistently outperforms both subset selection and other distillation baselines, demonstrating stronger robustness to rare samples.
Evaluation Model
Methods
IR@1
IR@5
IR@ 10
TR
TR@5
TR @ 10
K-center
8.5 114
26.7 209
40.0 =08
15.4 +09
40.3 +0s
54.0
Herding
9.4
28.7
43.7 =.7
1.2
38,8 +20
51.3 1.4
CLIP score
+.4
22.3 404
33.5 005
1..
30.6
42.9 =21
LAION filtering
6.6
20.2
31.8
8.8
24.3
36.3 11.
ResNet
Image-based
6.7 0.5
22.4
33.3
12.3
+0.2
42.1
TESLA-VL
11.0
35.0
50.9
15.0
39.6
52.5 +30
LoRS
14.1
37.2
54.0
22.6
47.4
62.2 223
PDS
23.2 +.8
58.4 006
74.5 #08
25.2
58.8 1.2
73.6
22

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Published as a conference paper at ICLR 2026
C.10 RESULT ON THE FLICKR&K DATASET
We further evaluate PDS on the Flickr&K (Hodosh et al., 2013) dataset and compare it against subset selection methods. As shown in Table 16, PDS consistently outperforms the subset selection baselines.
Table 16: Comparison of PDS with subset selection methods Flickr8K. We evaluate PDS and subset selection baselines in the 100-pair setting on Flickr8K, and PDS consistently outperforms all subset selection methods.
Evaluation Model
Methods
IR@1 1
IR@5
IR@10
TR@1
TR@5
TR@ 10
K-center
0.8 0.8
3.4
6.1
1.2
3,9 1.5
6,6
Herding
0..9
3.3
5.6
+
3.8 444
6.0
CLIP score
3.2
12.5 +0.2
19.4
5.5
14.4
2
ResNet
LAION filtering
3.1
13.0
20.4
6.4
22.6+002
Image-based
3.2
12.3 +.2
18.8
5.6
14.1 #06
21.9
PDS
+.2
2..0
39.8 =0.3
10.3 0.2
27.1 #0.7
38.3 =
C.I1 COMPARISON OF TEXT, IMAGE, AND AVERAGE PROTOTYPE CONDITIONING THE UNCLIP DECODER
We additionally generate images conditioned either on the text prototype or on the average of the text and image prototypes. As shown in Table 17, conditioning the text prototype results in degraded performance, while averaging the text and image prototypes produces results comparable conditioning the image prototype, with noticeable improvements.
Table 17: Comparison of conditioning text, image, and average prototypes. We evaluate different conditioning strategies in the 100-pair setting on Flickr30K. Conditioning on the text pro- totype degrades performance, while using the average of the and image prototypes achieves results comparable to conditioning on the image prototype, providing no additional gains.
Evaluation
Methods
IR@1
IR@5
IR@ 10
TR@1 1
TR@5
TR 10
Model
Text prototype
8.1 0.2
0.2
36.6 004
10.8
28.2
38.3 0.7
ResNet
Average prototype
022
26.2 #0.
37.9
0.5
39.4 = 0.3
Image prototype
7.9
20.
+7.3
10.2 0.3
28.2 0.9
39.0 0.3
Text prototype
5.6
17.3
26.4 0.1
4.6
16.3 0.7
23.9 =
VIT
Average prototype
5.1
18.5
27.7
6.5
18.4 0.3
27.3 =
Image prototype
6.8 0.3
1..2
28.5 + 0.4
6.6
17.5
26.9 0.5
C.12 COMPARISON OF JOINT AND SEPARATE CLUSTERING APPROACHES
is certainly possible to perform clustering directly a joint embedding space, but the two modal- ities can differ in how their embeddings distributed, which can sometimes cause the clustering to influenced more by one modality than the other. For this reason, we cluster each modality separately and then align the resulting clusters across modalities. As shown in Table 18, the two approaches perform comparably, and the separate clustering approach slightly better few cases.
23

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Table 18: Comparison of joint-embedding clustering (Joint) and separate clustering (Sepa- rate). We evaluate different clustering strategies by distilling the Flickr30K and MS-COCO into 100 and 300 pairs, and evaluating the resulting distilled sets using ResNet backbone. Overall, both clustering strategies perform comparably, with the separate clustering approach showing slight improvements in few cases.
Dataset
Pairs
Methods
IR@1
IR@5
IR@ 10
TR
TR@5
TR 10
Joint
8.3 0.1
25.3 002
36.8 20.4
11.0 0.4
29.5 a 0.4
40.7 = 0.4
100
Separate
0.9
25.8 + 0.4
37.3 0.3
10.2 +0.2
28.2 0.9
39.0 + 0.3
Flickr30K
Joint
14.6
38.8 0.3
52.3 0.4
17.5 0.4
43.5 a 0.4
56.4 a
300
Separate
14.4 0.4
38.1 #.2
51.4 0.4
18.7 = 0.5
45.0 a 0.4
57.8 0.6
Joint
2.5 #.1
10.2 + 0.2
17.5 0.3
4.1
12.9 a 0.2
20.6 a 0.2
100
Separate
2.8 0.1
10.0 0.2
17.3 0.3
4.5 0.2
14.0 * 0.3
21.4 # 0.4
MS-COCO
Joint
4.1
16.6
25.9
6.6
19.3 0.3
29,2 =
300
Separate
5.3
+ 0.4
#
7.4
20.7 = 0.3
30.2
D APPLICATION
D.I APPLICATION OF THE DISTILLED DATASET TO ASIF
We apply distilled dataset ASIF (Norelli et al., 2023), Although ASIF and PDS developed for different purposes, they are complementary. ASIF aligns pre-trained unimodal encoders with- additional training by computing relative representations whose elements are cosine similarities between a sample and each anchor in the anchor set. Consequently, the quality of the anchor set crucial, and the computational cost ASIF increases with the number of anchors. The original ASIF paper reports that more than one million anchors are required to reach CLIP-level perfor- mance, highlighting the importance identifying compact yet informative anchors.
PDS can address this point by producing small set of highly informative samples that can serve as stronger anchors than those obtained by selecting subsets of the original dataset. In Table 2 of manuscript, already show that PDS produces more representative samples than subset selection baselines. Based on this result, we evaluate the performance of ASIF with 300 anchors obtained from Flickr30K using (i) subset selection methods, (ii) other distillation methods, and (iii) PDS. For this comparison, we use ResNet image encoder and a CLIP text encoder to compute the relative representations, and performance is evaluated through retrieval accuracy Flickr30K. As shown in Table 19, the anchors produced by PDS lead to the strongest performance among all compared methods, implying that PDS allows ASIF to operate effectively with fewer anchors.
Table 19: Application of the distilled dataset as anchors ASIE To evaluate how well distilled or selected datasets for each method, including subset selection, other distillation approaches, and PDS. Using ResNet image encoder and CLIP text encoder to compute relative representations, accuracy on Flickr30K. Anchors produced by PDS consistently outperform those obtained from all other baselines, demonstrating that PDS yields more informative and effective anchors for ASIF.
compact anchors for ASIF, construct 300 anchors from Flickr30K
retrieval
Evaluation Model
Methods
IR@1
IR@5
IR@ 10
TR@1
TR@5
TR
K-center
3.8
14.4
24.2
4.0
14.8
22.7
Herding
6.0
21.5
32.5
6.9
19.1
28.1
CLIP
2.1
9.5
16.9
3.5
11.5
18.7
LAION filtering
3.1
11.2
17.9
3.4
12.5
17.4
ResNet
Image- image-based
2.3
9.6
17.1
4.1
12.6
18.3
TESLA-VL
5.3
19.4
29.8
5.0
16.8
25,9
LoRS
4.9
18.6
28.2
5.7
16.6
25.9
PDS
9.9
29.8
41.8
9.6
27.7
38.6
24

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Published as a conference paper at ICLR 2026
D.2 INTEGRATION OF PDS INTO OPTIMIZATION-BASEE BASED MULTIMODAL DISTILLATION
We integrate PDS into existing optimization-based multimodal distillation methods by using the PDS-distilled dataset tillation. Although replacing real samples used for initialization with the PDS-distilled dataset pro- vides more informative starting point, the subsequent optimization process leads an architecture- dependent distilled dataset, which undermines the tross-architecture generalization achieved by PDS. As shown in Table 20, initializing the optimization- based method with the PDS distilldl dataset tends to improve its performance, but it still underperforms compared to using PDS alone.
initialization, which is known be crucial for optimization-based dis-
Table 20: Integrating PDS into optimization-basel distillation baselines provides little advan- tage. We evaluate optimization-base multimodal distillation methods initialized with the PDS- distilled dataset in the 100-pair setting Flickr30K. Although this initialization provides a more informative starting point, the subsequent optimization makes the distilled dataset architecture- dependent, limiting the archttrcc generalization achieved by PDS. Consequently, while PDS initialization tends to improve these baselines, this integrated approach still underperforms compared to using PDS alone.
Evaluation Model
Method
IR @1
IR@5
IR@10
TR@1
TR@5
TR 10
TESLA-VL
4.1 +03
14.7 209
22,9 1.2
0.4
=1
27.3 = 14
TESLA-VL +PDS
0.4
17.7
27.5
1.5
22.4
33.1 11
ResNett
LoRS
6.3
18.6
28.0
9.1
24.3
34.5
LoRS+PDS
5.7
19.6
29,2
8.3
24.1
34.3
PDS
0.3
25.8
3..
10.2
2.9
39.0 0.3
25

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Published as a conference paper at ICLR 2026
blond woman in blue low cut shirt
man running looking path camera
man is pushing cart with baskets
young man holding
microphone and singing
group of people in colorful costumes uniforms performing
a man wearing gray jacket standing on the side
male sitting table with coffee cup front
lone person standing on some rocks with background
men standing on shore with fishing pole and beat water
two men standing on street wearing firemen's uniforms
a construction worker is standing on top of structure
two people are walking along the beach
man wears white turban outside
three people walking on rocks near stream
construction workers are working on a construction project
two femeles are looking at a notebook in fab
using computor while seated at a desk
young girl plnk shirt with toys around her
man walking street
pertorming for crowd of people
playing snow
taking picture with camera
group people standing around
from one side the pool to the other
Figure 7: Examples of synthetic images and retrieved captions in the 100-pair setting on Flickr30K.
26

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Published as a conference paper at ICLR 2026
blond woman wearing sunglasses on the top her
man is standing at the top of ladder against building
man is grilling food barbecue
woman standing on snowy hill about
woman In white is holding blue umbrella walking
woman shops for produce at an outdoor market
man In crowd wearing red shirt is holding
three children standing near huts
tennis player trying to hit the ball
two people sitting around campfire
man working with a welding tool
man riding cart being pulled by donkey
bicycle rides past big building
orange attire are cleaning up garbage the street
man harness climbing rock wall
mountain riding through forest
group people on trail through the forest
player catches the ball as another tries to tackle him
walking dogs
people walking path front of bridge
get
group bicyclists are all riding their bikes line on the street
married in wedding caramony
shoveling snow
jumping out of plane towards an airstrip below
Figure 8: Examples of synthetic images and retrieved captions in the 300-pair setting on Flickr30K.
27

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Published as a conference paper at ICLR 2026
person field throwing frisbee
people in park with variety
two giraffes standing together in
people near fruit stand with bunches of
man holding a tennis racket the tennis
a tall clock tower behind building with clock
many stuffed teddy bears are set next to another
people are on the beach with kites flying
a boy swinging baseball bat while on field
herd zebras standing grassy plain
horse grazing on grass in large open field
large airpiane on a runway at the airport
# laying an empty suitcase
bethroom with white sink under mirror
plate food with broccoli on it
some sheep standing and grazing in field
city with uniformed officer standing by
horse riding in front of horse
standing and grazing in fleld
at a station
elephants walking across field
furniture and
water near other bosts
side street
television
Figure 9; Examples of synthetic images and retrieved captions in the 100-pair setting on MS-COCO.
28

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Published as a conference paper at ICLR 2026
hydrant
people riding horses on the sand beach
man standing fruit stand carrying bunches of bananas
a tv and watches
street in the snow
pulling carriage down a street
player attempts the ball
and tub and tiled walls
near trees and bushes
group people the beach with surfboards
open umbralla beach chairs
hard standing open grass field
group people skiiing up snow covered slope
bunches hanging tree
traveling way
different parked display runway glass
airport
herd of cows grazing grass
bowl full sitting wooden
sitting elephent sticking his outside an enclosure
woman walking across street holding
giraffes
variety of fruit beiing sold market
shoop standing around
Figure 10: Examples of synthetic images and retrieved captions in the 300-pair setting on MS- COCO.
29


Reviews:
[{"review_id": "PRSpNczbcm", "reviewer": "Reviewer_Td75", "summary": "The aim of this paper is to summarize big multimodal datasets in smaller sizes datasets of hundreds of pairs while losing as little performance as possible. This idea of dataset distillation is old, but has not been explored for multimodal datasets made of pairs. Indeed, these datasets are used as a bridge between two domains and the idea is to make this bridge very light and portable.", "questions": "- Just a curiosity, have you tried to use unCLIP on the text embedding used in place of the image embedding? I would bet that performance is worse, but maybe not, I am not confident. Perhaps an average of the two is even better?\n- One thing that surprised me is that you had clusters with no overlapping pairs at all. How many clusters? Also, I was expecting you to discard them since you cannot do the average, but instead you keep the centroid. Perhaps you want to motivate this choice more, since my first thought as a reader was that you would have discarded them.\n\n---\n\nOverall, I think the topic is important and the idea is interesting, but you could make it better shine! I will set my score at 6 with the hope of raising it.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The core concept is both timely and important. The paper correctly identifies that summarizing a link between domains is a different and more nuanced problem than summarizing a single domain. This is a valuable contribution to the field.\n- The procedure described is logical and well-conceived. The idea of selecting only the overlapping pairs within matched clusters to form prototypes is a particularly clever mechanism for strengthening cross-modal alignment.", "weaknesses": "- A thing I found less convincing is what to do with these distilled, let’s say, 300 pairs. From what I understood it is not possible to use them to train a CLIP-like model from scratch (it would have been cool…).\n- The application suggested—to use these pairs to link a vision space with a text space through a fine-tuned linear layer—is weaker than it seems. The two spaces are often already very aligned (see e.g., e.g., Huh et al., 2024, \"The Platonic Representation Hypothesis\"), so it's reasonable to expect they do not need much to be paired. Consequently, the authors should compare their method not only to other distillation techniques but also to methods specifically for multimodal latent space alignment, such as the relative representation alignment in ASIF (Norelli et al., 2023), that already uses far less pairs than usual multimodal datasets.\n\nA cool application of the method I see is to make techniques like ASIF work with even less data, that is: hundreds of pairs. That would be very cool; the reduced dataset would also foster interpretability besides speed and privacy.\n\n- The comparison with other data distillation techniques does not seem entirely fair, since this method saves a text embedding and not a text for each pair, which is significantly more information. In this sense, it would have been good to show performance also of the method selecting the closest caption to the embedding, especially since the authors already retrieve this exact caption to condition the unCLIP image generator.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "8zKjyCephG", "reviewer": "Reviewer_1wEY", "summary": "The paper proposes the Prototype-Guided Data Synthesis (PDS) framework, a learning-free dataset distillation framework that eliminates large-scale training and optimization based techniques. The framework uses a pre-trained CLIP multimodal model to obtain image and text embeddings, the embeddings are pruned based on a similarity score and clustered to capture the dataset's semantic diversity through a Mini-Batch K-Means algorithm and matched using the Hungarian algorithm. An unCLIP decoder model is then used on the CLIP image embeddings to generate a synthetic image by retrieving the most similar caption most similar to the text prototype from the training set. The PDS framework outperforms multimodal dataset distillation baseline such as TESLA-VL and LoRS, also the framework outperforms dataset subset selection method such as Herding.", "questions": "- Can the PDS framework improvements over state-of-the-art methods be reproduced by using different CLIP variants? For example, clip-ViT-B-32 or clip-ViT-B-16? \n- Are the authors planning to release the code to reproduce the results reported in the paper? Please include scripts to reproduce all tables/figures. \n- Which unCLIP decoder is used? How sensitive are the results to the decoder's guidance scale and sampling step hyperparameters? \n- Could the sensitivity of PDS to the similarity-pruning threshold, the clustering seed, and the number of desired distilled samples (M) be assessed, with ablation studies on these factors?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper proposes a novel dataset distillation method based on a pre-trained CLIP encoder and unCLIP decoder to extract image embeddings. These extract embeddings are then forwarded in an unCLIP decoder to generate a distilled dataset. The paper's methodological presentation and its contributions are well articulated in the text. Empirically, PDS achieves state-of-the-art performance compared with dataset subset selection and multimodal dataset distillation baselines, demonstrating the advantages of the proposed approach.", "weaknesses": "- The paper's presented  PDS framework is evaluated only with ViT-L/14 CLIP encoders. \n- The code to replicate results is not yet released (even anonymously).\n- Which unCLIP decoder is used in the PDS framework? Currently, the authors provide a citation to Ho & Salimans (2022) and the guidance scale and sampling step hyperparameters, without specifying the model architecture used.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "ulI99mxZzr", "reviewer": "Reviewer_WZ3d", "summary": "The paper proposes an approach to dataset generation in order to train multi-modal (text and image) models on a more compact amount of data, thus requiring less compute and energy. Different from existing distillation methods based on the optimization paths of the models, this paper looks at the latent space of an existing multimodal model CLIP and generates paired (image-text) samples directly from it after clustering. The experimental evaluation shows advancement over existing methods of dataset distillation.", "questions": "1 - Why sampling inputs from the latent space clusters will lead to reduction in the data required for training a good model? Current empirical results show that the difference between performance on the full dataset and the synthesized one is significant. Is it even possible to catch up with the original performance in this way?\n\n2 - How one can guarantee that generated samples indeed will be significantly different from the copyright data, if that one was used in training CLIP mode?\n\n3 - How do you select amount of clusters for generating \"prototypes\"?\n\n4 - Why not to perform clustering right away in the joint space of representations, but try to map clusters on each other later?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The work proposes a strong case for need of more compact and easier produced datasets for multi-model training. The provided experiments are extensive and include possible ablation studies.", "weaknesses": "I am wondering if it is possible to prove in any way, that the learning result from such distilled\\synthesized dataset is similar to the original learning result. The resulting performance is only a weak sign of similarity of the trained models.\n\nAlso, I find it contradictory, that the motivation for the proposed method is based on the need for large-scale dataset existing beforehand, while this method as well requires trained CLIP-model, which means that this large scale dataset was already used as well.\n\nFinally, I find the argument about generating samples that are sufficiently different from the copyright prohibited images rather weak. First of all, it does not remove the copyright prohibited images from the original CLIP model, second there were no strict metrics provided proving that it is indeed the case that images would sufficiently differ compared to other methods (except for one Figure).\n\nMinor:\n\n- Referncing to tables and figures in Section3 is a bit cryptic - maybe more details on how exactly table proves the point would be helpful for the reader, who does not have to go to the tables beforehand then.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "vn3aivazko", "reviewer": "Reviewer_x6Kp", "summary": "The paper proposes a learning-free framework for multimodal dataset distillation, which avoids complex optimization by leveraging pre-trained models such as CLIP and unCLIP generation models, with new designs. The experiments validate the effectiveness.", "questions": "1. The results show a large effect of the distilled size ($M$) on performance (comparing $M=100$ and $M=300$). Can the authors provide a broader analysis of performance scaling as $M$ increases? More importantly, what practical guidance should be used to select a suitable $M$ for a new distillation problem?\n\n2. The method relies heavily on pre-trained models (CLIP and unCLIP). It is unclear how much of the success is due to the PDS framework versus the prior knowledge embedded in these large foundation models. Could the authors comment on this dependency?\n\n3. Could the proposed Learning-Free PDS approach be integrated with existing optimization-based methods? Combining PDS's efficient initialization with the precision of optimization could lead to a new state-of-the-art in distilled dataset quality.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The intuition is clear and novel. Instead of using costly optimization-based approaches for dataset distillation, the paper propose to reuse pre-trained models such as CLIP, unCLIP to derive the underlying multimodal embeddings, and use that for later dataset generation. This provides an inspiring direction  for multimodal dataset distillation.\n2. The results show that PDS outperforms other baselines that are optimization-based, which shows good cross-architecture generalisation ability.", "weaknesses": "1. The \"learning-free\" claim needs qualification. While the distillation process itself avoids optimization, the framework is critically dependent on specific pre-trained models: CLIP and an unCLIP decoder. This reliance on a specialized generative model capable of conditioning on CLIP image embeddings may hinder the method's general usability.\n2. The model may loss ability to use newer embeddings since it requires an generative model that can accept this embedding as as condition. This constraint may limit the model's performance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "0nvnhKnJUO", "reviewer": "Reviewer_gG1N", "summary": "This paper proposes a prototype-guided data synthesis framework for multimodal dataset distillation that removes the need for large-scale training and optimization. It achieves efficient and scalable multimodal data generation, outperforming previous optimization-based methods while improving cross-architecture generalization.", "questions": "Please refer to 'Weaknesses'\n\n1. Will averaging embeddings to construct prototypes cause degraded representations?\n\n2. The method uses the unCLIP decoder to reconstruct images from CLIP embeddings/text. The capibility of the decoder could be essential. Do authors observe failure cases and how to process them? \n\n3. Besides Flickr30K and MS-COCO, have authors considered other data?\n\n4. While the major classes are well represented in the proposed prototypes, have authors considered the performance on rare or long-tail classes?", "limitations": "", "rating": 4, "confidence": 2, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The proposed method is learning-free, making it simple, efficient, and architecture-independent.\n2. The framework works well across different architectures, showing cross-architecture generalization.\n3. It synthesizes semantically aligned image–text pairs, improving multimodal learning performance.", "weaknesses": "1. Will averaging embeddings to construct prototypes cause degraded representations?\n\n2. The method uses the unCLIP decoder to reconstruct images from CLIP embeddings/text. The capibility of the decoder could be essential. Do authors observe failure cases and how to process them? \n\n3. Besides Flickr30K and MS-COCO, have authors considered other data?\n\n4. While the major classes are well represented in the proposed prototypes, have authors considered the performance on rare or long-tail classes?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)