Dataset Distillation

(Tongzhou Wang et al., 2018) :globe_with_meridians: :octocat: :book:

Gradient-Based Hyperparameter Optimization Through Reversible Learning

(Dougal Maclaurin et al., ICML 2015) :octocat: :book:

Dataset Condensation with Gradient Matching

(Bo Zhao et al., ICLR 2021) :octocat: :book:

Dataset Condensation with Differentiable Siamese Augmentation

(Bo Zhao et al., ICML 2021) :octocat: :book:

Dataset Distillation by Matching Training Trajectories

(George Cazenavette et al., CVPR 2022) :globe_with_meridians: :octocat: :book:

Dataset Condensation with Contrastive Signals

(Saehyung Lee et al., ICML 2022) :octocat: :book:

Loss-Curvature Matching for Dataset Selection and Condensation

(Seungjae Shin & Heesun Bae et al., AISTATS 2023) :octocat: :book:

Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation

(Jiawei Du & Yidi Jiang et al., CVPR 2023) :octocat: :book:

Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory

(Justin Cui et al., ICML 2023) :octocat: :book:

Sequential Subset Matching for Dataset Distillation

(Jiawei Du et al., NeurIPS 2023) :octocat: :book:

Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching

(Ziyao Guo & Kai Wang et al., ICLR 2024) :globe_with_meridians: :octocat: :book:

SelMatch: Effectively Scaling Up Dataset Distillation via Selection-Based Initialization and Partial Updates by…

(Yongmin Lee et al., ICML 2024) :octocat: :book:

Dataset Distillation by Automatic Training Trajectories

(Dai Liu et al., ECCV 2024) :octocat: :book:

Neural Spectral Decomposition for Dataset Distillation

(Shaolei Yang et al., ECCV 2024) :octocat: :book:

Prioritize Alignment in Dataset Distillation

(Zekai Li & Ziyao Guo et al., 2024) :octocat: :book:

Towards Stable and Storage-efficient Dataset Distillation: Matching Convexified Trajectory

(Wenliang Zhong et al., CVPR 2025) :octocat: :book:

Emphasizing Discriminative Features for Dataset Distillation in Complex Scenarios

(Kai Wang & Zekai Li et al., CVPR 2025) :octocat: :book:

CAFE: Learning to Condense Dataset by Aligning Features

(Kai Wang & Bo Zhao et al., CVPR 2022) :octocat: :book:

Dataset Condensation with Distribution Matching

(Bo Zhao et al., WACV 2023) :octocat: :book:

Improved Distribution Matching for Dataset Condensation

(Ganlong Zhao et al., CVPR 2023) :octocat: :book:

DataDAM: Efficient Dataset Distillation with Attention Matching

(Ahmad Sajedi & Samir Khaki et al., ICCV 2023) :globe_with_meridians: :octocat: :book:

M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy

(Hansong Zhang & Shikun Li et al., AAAI 2024) :octocat: :book:

Exploiting Inter-sample and Inter-feature Relations in Dataset Distillation

(Wenxiao Deng et al., CVPR 2024) :octocat: :book:

Dataset Condensation with Latent Quantile Matching

(Wei Wei et al., CVPR 2024 Workshop) :book:

DANCE: Dual-View Distribution Alignment for Dataset Condensation

(Hansong Zhang et al., IJCAI 2024) :octocat: :book:

Diversified Semantic Distribution Matching for Dataset Distillation

(Hongcheng Li et al., MM 2024) :octocat: :book:

Dataset Distillation with Neural Characteristic Function: A Minmax Perspective

(Shaobo Wang et al., CVPR 2025) :octocat: :book:

OPTICAL: Leveraging Optimal Transport for Contribution Allocation in Dataset Distillation

(Xiao Cui et al., CVPR 2025) :book:

Dataset Distillation via the Wasserstein Metric

(Haoyang Liu et al., ICCV 2025) :globe_with_meridians: :octocat: :book:

Diversity-Enhanced Distribution Alignment for Dataset Distillation

(Hongcheng Li et al., ICCV 2025) :book:

Hyperbolic Dataset Distillation

(Wenyuan Li & Guang Li et al., NeurIPS 2025) :globe_with_meridians: :octocat: :book:

TGDD: Trajectory Guided Dataset Distillation with Balanced Distribution

(Fengli Ran et al., AAAI 2026) :octocat: :book:

GeoDM: Geometry-aware Distribution Matching for Dataset Distillation

(Xuhui Li et al., ICML 2026) :book:

Dataset Meta-Learning from Kernel Ridge-Regression

(Timothy Nguyen et al., ICLR 2021) :octocat: :book:

Dataset Distillation with Infinitely Wide Convolutional Networks

(Timothy Nguyen et al., NeurIPS 2021) :octocat: :book:

Dataset Distillation using Neural Feature Regression

(Yongchao Zhou et al., NeurIPS 2022) :globe_with_meridians: :octocat: :book:

Efficient Dataset Distillation using Random Feature Approximation

(Noel Loo et al., NeurIPS 2022) :octocat: :book:

Dataset Distillation with Convexified Implicit Gradients

(Noel Loo et al., ICML 2023) :octocat: :book:

Provable and Efficient Dataset Distillation for Kernel Ridge Regression

(Yilan Chen et al., NeurIPS 2024) :book:

Dataset Condensation via Efficient Synthetic-Data Parameterization

(Jang-Hyun Kim et al., ICML 2022) :octocat: :book:

Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks

(Zhiwei Deng et al., NeurIPS 2022) :octocat: :book:

On Divergence Measures for Bayesian Pseudocoresets

(Balhae Kim et al., NeurIPS 2022) :octocat: :book:

Dataset Distillation via Factorization

(Songhua Liu et al., NeurIPS 2022) :octocat: :book:

Dataset Condensation with Latent Space Knowledge Factorization and Sharing

(Hae Beom Lee & Dong Bok Lee et al., 2022) :book:

Slimmable Dataset Condensation

(Songhua Liu et al., CVPR 2023) :book:

PRANC: Pseudo RAndom Networks for Compacting Deep Models

(Parsa Nooralinejad et al., ICCV 2023) :octocat: :book:

Few-Shot Dataset Distillation via Translative Pre-Training

(Songhua Liu et al., ICCV 2023) :book:

MGDD: A Meta Generator for Fast Dataset Distillation

(Songhua Liu et al., NeurIPS 2023) :book:

Sparse Parameterization for Epitomic Dataset Distillation

(Xing Wei & Anjia Cao et al., NeurIPS 2023) :octocat: :book:

Frequency Domain-based Dataset Distillation

(Donghyeok Shin & Seungjae Shin et al., NeurIPS 2023) :octocat: :book:

Leveraging Hierarchical Feature Sharing for Efficient Dataset Condensation

(Haizhong Zheng et al., ECCV 2024) :book:

FYI: Flip Your Images for Dataset Distillation

(Byunggwan Son et al., ECCV 2024) :globe_with_meridians: :octocat: :book:

Color-Oriented Redundancy Reduction in Dataset Distillation

(Bowen Yuan et al., NeurIPS 2024) :octocat: :book:

Distilling Dataset into Neural Field

(Donghyeok Shin et al., ICLR 2025) :octocat: :book:

Dataset Distillation as Data Compression: A Rate-Utility Perspective

(Youneng Bao & Yiping Liu et al., ICCV 2025) :globe_with_meridians: :octocat: :book:

Beyond Pixels: Efficient Dataset Distillation via Sparse Gaussian Representation

(Chenyang Jiang et al., 2025) :octocat: :book:

Post Training Quantization for Efficient Dataset Condensation

(Linh-Tam Tran et al., AAAI 2026) :book:

Synthesizing Informative Training Samples with GAN

(Bo Zhao et al., NeurIPS 2022 Workshop) :octocat: :book:

Generalizing Dataset Distillation via Deep Generative Prior

(George Cazenavette et al., CVPR 2023) :globe_with_meridians: :octocat: :book:

DiM: Distilling Dataset into Generative Model

(Kai Wang & Jianyang Gu et al., 2023) :octocat: :book:

Dataset Condensation via Generative Model

(Junhao Zhang et al., 2023) :book:

Generative Dataset Distillation: Balancing Global Structure and Local Details

(Longzhen Li & Guang Li et al., CVPR 2024 Workshop) :book:

Data-to-Model Distillation: Data-Efficient Learning Framework

(Ahmad Sajedi & Samir Khaki et al., ECCV 2024) :book:

Generative Dataset Distillation Based on Self-knowledge Distillation

(Longzhen Li & Guang Li et al., ICASSP 2025) :book:

Hierarchical Features Matter: A Deep Exploration of GAN Priors for Improved Dataset Distillation

(Xinhao Zhong & Hao Fang et al., CVPR 2025) :octocat: :book:

Efficient Dataset Distillation via Minimax Diffusion

(Jianyang Gu et al., CVPR 2024) :octocat: :book:

D4M: Dataset Distillation via Disentangled Diffusion Model

(Duo Su & Junjie Hou et al., CVPR 2024) :globe_with_meridians: :octocat: :book:

Generative Dataset Distillation Based on Diffusion Model

(Duo Su & Junjie Hou & Guang Li et al., ECCV 2024 Workshop) :octocat: :book:

Influence-Guided Diffusion for Dataset Distillation

(Mingyang Chen et al., ICLR 2025) :octocat: :book:

Taming Diffusion for Dataset Distillation with High Representativeness

(Lin Zhao et al., ICML 2025) :octocat: :book:

MGD3: Mode-Guided Dataset Distillation using Diffusion Models

(Jeffrey A. Chan-Santiago et al., ICML 2025) :globe_with_meridians: :octocat: :book:

Enhancing Diffusion-based Dataset Distillation via Adversary-Guided Curriculum Sampling

(Lexiao Zou et al., ICME 2025) :book:

CaO2: Rectifying Inconsistencies in Diffusion-Based Dataset Distillation

(Haoxuan Wang et al., ICCV 2025) :octocat: :book:

Dataset Distillation via Vision-Language Category Prototype

(Yawen Zou & Guang Li et al., ICCV 2025) :globe_with_meridians: :octocat: :book:

Task-Specific Generative Dataset Distillation with Difficulty-Guided Sampling

(Mingzhuo Li & Guang Li et al., ICCV 2025 Workshop) :octocat: :book:

Unlocking Dataset Distillation with Diffusion Models

(Brian B. Moser & Federico Raue et al., NeurIPS 2025) :octocat: :book:

Optimizing Distributional Geometry Alignment with Optimal Transport for Generative Dataset Distillation

(Xiao Cui et al., NeurIPS 2025) :book:

Dataset Condensation with Color Compensation

(Huyu Wu et al., TMLR 2025) :globe_with_meridians: :octocat: :book:

Diffusion Models as Dataset Distillation Priors

(Duo Su et al., ICLR 2026) :book:

CoDA: From Text-to-Image Diffusion Models to Training-Free Dataset Distillation

(Letian Zhou et al., ICLR 2026) :octocat: :book:

ManifoldGD: Training-Free Hierarchical Manifold Guidance for Diffusion-Based Dataset Distillation

(Ayush Roy et al., CVPR 2026) :octocat: :book:

IMS3: Breaking Distributional Aggregation in Diffusion-Based Dataset Distillation

(Chenru Wang & Yunyi Chen et al., CVPR 2026) :book:

EVLF: Early Vision-Language Fusion for Generative Dataset Distillation

(Wenqi Cai et al., CVPR 2026) :globe_with_meridians: :octocat: :book:

Learnability-Guided Diffusion for Dataset Distillation

(Jeffrey A. Chan-Santiago et al., CVPR 2026) :globe_with_meridians: :book:

DMGD: Train-Free Dataset Distillation with Semantic-Distribution Matching in Diffusion Models

(Qichao Wang et al., CVPR 2026) :book:

Mitigating the Distribution Shift of Diffusion-based Dataset Distillation

(Yue Xu et al., CVPR 2026) :book:

Geometry-Aware Dataset Condensation for Diffusion Model Training

(Xiao Cui et al., ICML 2026) :octocat: :book:

DIVER: Diving Deeper into Distilled Data via Expressive Semantic Recovery

(Qianxin Xia et al., ICML 2026) :octocat: :book:

Set-Coupled Guidance: Set-Level Coordination in Diffusion-Based Dataset Distillation

(Ziang Gan et al., ICML 2026) :octocat: :book:

HIERAMP: Coarse-to-Fine Autoregressive Amplification for Generative Dataset Distillation

(Lin Zhao & Xinru Jiang et al., CVPR 2026) :octocat: :book:

ProtoVAR: Efficient Dataset Distillation via Prototype-Guided Visual Autoregressive Modeling

(Mingyu Wang et al., ICML 2026) :book:

Path-Guided Flow Matching for Dataset Distillation

(Xuhui Li et al., 2026) :book:

Accelerating Dataset Distillation via Model Augmentation

(Lei Zhang & Jie Zhang et al., CVPR 2023) :octocat: :book:

DREAM: Efficient Dataset Distillation by Representative Matching

(Yanqing Liu & Jianyang Gu & Kai Wang et al., ICCV 2023) :octocat: :book:

You Only Condense Once: Two Rules for Pruning Condensed Datasets

(Yang He et al., NeurIPS 2023) :octocat: :book:

MIM4DD: Mutual Information Maximization for Dataset Distillation

(Yuzhang Shang et al., NeurIPS 2023) :book:

Can Pre-Trained Models Assist in Dataset Distillation?

(Yao Lu et al., 2023) :octocat: :book:

DREAM+: Efficient Dataset Distillation by Bidirectional Representative Matching

(Yanqing Liu & Jianyang Gu & Kai Wang et al., 2023) :octocat: :book:

Dataset Distillation in Latent Space

(Yuxuan Duan et al., 2023) :book:

Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality

(Xuxi Chen & Yu Yang et al., ICLR 2024) :octocat: :book:

Embarassingly Simple Dataset Distillation

(Yunzhen Feng et al., ICLR 2024) :octocat: :book:

Multisize Dataset Condensation

(Yang He et al., ICLR 2024) :octocat: :book:

Large Scale Dataset Distillation with Domain Shift

(Noel Loo & Alaa Maalouf et al., ICML 2024) :octocat: :book:

Distill Gold from Massive Ores: Bi-level Data Pruning towards Efficient Dataset Distillation

(Yue Xu et al., ECCV 2024) :octocat: :book:

Towards Model-Agnostic Dataset Condensation by Heterogeneous Models

(Jun-Yeong Moon et al., ECCV 2024) :octocat: :book:

Teddy: Efficient Large-Scale Dataset Distillation via Taylor-Approximated Matching

(Ruonan Yu et al., ECCV 2024) :book:

BACON: Bayesian Optimal Condensation Framework for Dataset Distillation

(Zheng Zhou et al., 2024) :octocat: :book:

Going Beyond Feature Similarity: Effective Dataset Distillation based on Class-aware Conditional Mutual Information

(Xinhao Zhong et al., ICLR 2025) :octocat: :book:

Curriculum Coarse-to-Fine Selection for High-IPC Dataset Distillation

(Yanda Chen & Gongwei Chen et al., CVPR 2025) :octocat: :book:

Not All Samples Should Be Utilized Equally: Towards Understanding and Improving Dataset Distillation

(Shaobo Wang et al., CVPR 2025 Workshop) :book:

Beyond Random: Automatic Inner-loop Optimization in Dataset Distillation

(Muquan Li et al., NeurIPS 2025) :book:

Dataset Distillation as Pushforward Optimal Quantization

(Hongye Tan et al., ICLR 2026) :book:

Dataset Distillation by Influence Matching

(Haoru Tan & Wang Wang et al., CVPR 2026) :octocat: :book:

Optimizing Millions of Hyperparameters by Implicit Differentiation

(Jonathan Lorraine et al., AISTATS 2020) :octocat: :book:

On Implicit Bias in Overparameterized Bilevel Optimization

(Paul Vicol et al., ICML 2022) :book:

On the Size and Approximation Error of Distilled Sets

(Alaa Maalouf & Murad Tukan et al., NeurIPS 2023) :book:

A Theoretical Study of Dataset Distillation

(Zachary Izzo et al., NeurIPS 2023 Workshop) :book:

What is Dataset Distillation Learning?

(William Yang et al., ICML 2024) :octocat: :book:

Mitigating Bias in Dataset Distillation

(Justin Cui et al., ICML 2024) :book:

Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learning

(Vyacheslav Kungurtsev et al., 2024) :book:

Flowing Datasets with Wasserstein over Wasserstein Gradient Flows

(Clément Bonet & Christophe Vauthier et al., ICML 2025) :octocat: :book:

Information-Guided Diffusion Sampling for Dataset Distillation

(Linfeng Ye et al., NeurIPS 2025 Workshop) :book:

A Discrepancy-Based Perspective on Dataset Condensation

(Tong Chen et al., 2025) :book:

Understanding Dataset Distillation via Spectral Filtering

(Deyu Bo et al., ICLR 2026) :book:

Dataset Distillation for Memorized Data: Soft Labels can Leak Held-Out Teacher Knowledge

(Freya Behrens et al., ICLR 2026) :book:

Rethinking Dataset Distillation: Hard Truths about Soft Labels

(Priyam Dey & Aditya Sahdev et al., CVPR 2026) :book:

Dataset Distillation Efficiently Encodes Low-Dimensional Representations from Gradient-Based Learning of Non-Linear…

(Yuri Kinoshita et al., ICML 2026) :book:

Utility Boundary of Dataset Distillation: Scaling and Configuration-Coverage Laws

(Zhengquan Luo et al., ICML 2026) :book:

Structural Assessment for Understanding and Guiding Dataset Distillation in Discrete Token Space

(Yue Cao et al., ECCV 2026) :book:

Rethinking Dataset Distillation for Classification: Do Distilled Sets Outperform Coresets?

(Trisha Mittal & Akshay Mehra et al., 2026) :book:

Flexible Dataset Distillation: Learn Labels Instead of Images

(Ondrej Bohdal et al., NeurIPS 2020 Workshop) :octocat: :book:

Soft-Label Dataset Distillation and Text Dataset Distillation

(Ilia Sucholutsky et al., IJCNN 2021) :octocat: :book:

A Label is Worth a Thousand Images in Dataset Distillation

(Tian Qin et al., NeurIPS 2024) :octocat: :book:

Are Large-scale Soft Labels Necessary for Large-scale Dataset Distillation?

(Lingao Xiao et al., NeurIPS 2024) :octocat: :book:

DRUPI: Dataset Reduction Using Privileged Information

(Shaobo Wang et al., 2024) :book:

Label-Augmented Dataset Distillation

(Seoungyoon Kang & Youngsun Lim et al., WACV 2025) :book:

GIFT: Unlocking Full Potential of Labels in Distilled Dataset at Near-zero Cost

(Xinyi Shang & Peng Sun et al., ICLR 2025) :octocat: :book:

Heavy Labels Out! Dataset Distillation with Label Space Lightening

(Ruonan Yu et al., ICCV 2025) :book:

Soft Label Pruning and Quantization for Large-Scale Dataset Distillation

(Lingao Xiao et al., TPAMI 2026) :octocat: :book:

Dataset Quantization

(Daquan Zhou & Kai Wang & Jianyang Gu et al., ICCV 2023) :octocat: :book:

Dataset Quantization with Active Learning based Adaptive Sampling

(Zhenghao Zhao et al., ECCV 2024) :octocat: :book:

Adaptive Dataset Quantization

(Muquan Li et al., AAAI 2025) :book:

Dataset Color Quantization: A Training-Oriented Framework for Dataset-Level Compression

(Chenyue Yu et al., ICLR 2026) :book:

Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective

(Zeyuan Yin & Zhiqiang Shen et al., NeurIPS 2023) :globe_with_meridians: :octocat: :book:

Dataset Distillation via Curriculum Data Synthesis in Large Data Era

(Zeyuan Yin et al., TMLR 2024) :octocat: :book:

Generalized Large-Scale Data Condensation via Various Backbone and Statistical Matching

(Shitong Shao et al., CVPR 2024) :octocat: :book:

On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm

(Peng Sun et al., CVPR 2024) :octocat: :book:

Information Compensation: A Fix for Any-scale Dataset Distillation

(Peng Sun et al., ICLR 2024 Workshop) :book:

Elucidating the Design Space of Dataset Condensation

(Shitong Shao et al., NeurIPS 2024) :octocat: :book:

Diversity-Driven Synthesis: Enhancing Dataset Distillation through Directed Weight Adjustment

(Jiawei Du et al., NeurIPS 2024) :octocat: :book:

Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator

(Xin Zhang et al., ICLR 2025) :octocat: :book:

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

(Zhiqiang Shen & Ammar Sherif et al., CVPR 2025) :octocat: :book:

Enhancing Dataset Distillation via Non-Critical Region Refinement

(Minh-Tuan Tran et al., CVPR 2025) :octocat: :book:

Curriculum Dataset Distillation

(Zhiheng Ma & Anjia Cao et al., TIP 2025) :octocat: :book:

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation

(Jiacheng Cui & Xinyue Bi et al., NeurIPS 2025) :octocat: :book:

FocusDD: Real-World Scene Infusion for Robust Dataset Distillation

(Youbin Hu et al., 2025) :book:

Dataset Distillation via Committee Voting

(Jiacheng Cui et al., 2025) :octocat: :book:

PRISM: Diversifying Dataset Distillation by Decoupling Architectural Priors

(Brian B. Moser et al., TMLR 2026) :octocat: :book:

DiRe: Diversity-promoting Regularization for Dataset Condensation

(Saumyaranjan Mohanty et al., WACV 2026) :octocat: :book:

Grounding and Enhancing Informativeness and Utility in Dataset Distillation

(Shaobo Wang et al., ICLR 2026) :book:

Fixed Anchors Are Not Enough: Dynamic Retrieval and Persistent Homology for Dataset Distillation

(Muquan Li et al., CVPR 2026) :book:

Balanced Dataset Distillation via Modeling Multiple Visual Pattern Distribution

(Guanghui Shi et al., CVPR 2026) :octocat: :book:

Beyond Soft Label: Dataset Distillation via Orthogonal Gradient Matching

(Deyu Bo et al., CVPR 2026) :book:

Condensing Large-Scale Datasets Directly with Minimal Information Loss

(Xinyi Shang & Peng Sun & Bei Shi et al., ECCV 2026) :octocat: :book:

Hard Labels In! Rethinking the Role of Hard Labels in Mitigating Local Semantic Drift

(Jiacheng Cui et al., ICML 2026) :octocat: :book:

Vision-Language Dataset Distillation

(Xindi Wu et al., TMLR 2024) :globe_with_meridians: :octocat: :book:

Low-Rank Similarity Mining for Multimodal Dataset Distillation

(Yue Xu et al., ICML 2024) :octocat: :book:

Audio-Visual Dataset Distillation

(Saksham Singh Kushwaha et al., TMLR 2024) :octocat: :book:

Beyond Modality Collapse: Representations Blending for Multimodal Dataset Distillation

(Xin Zhang et al., NeurIPS 2025) :book:

Efficient Multimodal Dataset Distillation via Generative Models

(Zhenghao Zhao et al., NeurIPS 2025) :octocat: :book:

CovMatch: Cross-Covariance Guided Multimodal Dataset Distillation with Trainable Text Encoder

(Yongmin Lee et al., NeurIPS 2025) :octocat: :book:

Decoupled Audio-Visual Dataset Distillation

(Wenyuan Li & Guang Li et al., 2025) :book:

ImageBindDC: Compressing Multi-modal Data with ImageBind-based Condensation

(Yue Min & Shaobo Wang et al., AAAI 2026) :book:

Multimodal Dataset Distillation Made Simple by Prototype-Guided Data Synthesis

(Junhyeok Choi et al., ICLR 2026) :octocat: :book:

Multimodal Dataset Distillation via Phased Teacher Models

(Shengbin Guo & Hang Zhao et al., ICLR 2026) :octocat: :book:

Asynchronous Matching with Dynamic Sampling for Multimodal Dataset Distillation

(Ding Qi et al., ICLR 2026) :book:

Multimodal Distribution Matching for Vision-Language Dataset Distillation

(Jongoh Jeong & Hoyong Kwon & Minseok Kim et al., CVPR 2026) :octocat: :book:

Efficient Multi-modal Dataset Distillation via Analytic Parameter Matching

(Deyu Bo et al., ICML 2026) :octocat: :book:

Rank-Aware Hyperbolic Alignment for Vision-Language Dataset Distillation

(Jongoh Jeong et al., ECCV 2026) :globe_with_meridians: :octocat: :book:

Self-Supervised Dataset Distillation for Transfer Learning

(Dong Bok Lee & Seanie Lee et al., ICLR 2024) :octocat: :book:

Efficiency for Free: Ideal Data Are Transportable Representations

(Peng Sun et al., NeurIPS 2024) :octocat: :book:

Self-supervised Dataset Distillation: A Good Compression Is All You Need

(Muxin Zhou et al., 2024) :octocat: :book:

Dataset Distillation via Knowledge Distillation: Towards Efficient Self-Supervised Pre-Training of Deep Networks

(Siddharth Joshi et al., ICLR 2025) :octocat: :book:

Boost Self-Supervised Dataset Distillation via Parameterization, Predefined Augmentation, and Approximation

(Sheng-Feng Yu et al., ICLR 2025) :book:

Dataset Distillation for Pre-Trained Self-Supervised Vision Models

(George Cazenavette et al., NeurIPS 2025) :globe_with_meridians: :octocat: :book:

Closed-Form Linear-Probe Dataset Distillation for Pre-trained Vision Models

(Bincheng Peng & Guang Li et al., 2026) :book:

DC-BENCH: Dataset Condensation Benchmark

(Justin Cui et al., NeurIPS 2022) :globe_with_meridians: :octocat: :book:

A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness

(Zongxiong Chen & Jiahui Geng et al., 2023) :book:

BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation

(Zheng Zhou et al., 2024) :globe_with_meridians: :octocat: :book:

DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation

(Yifan Wu et al., TIP 2025) :octocat: :book:

DD-Ranking: Rethinking the Evaluation of Dataset Distillation

(Zekai Li & Xinhao Zhong et al., 2025) :globe_with_meridians: :octocat: :book:

Rectified Decoupled Dataset Distillation: A Closer Look for Fair and Comprehensive Evaluation

(Xinhao Zhong et al., ICLR 2026) :book:

Data Distillation: A Survey

(Noveen Sachdeva et al., TMLR 2023) :book:

A Survey on Dataset Distillation: Approaches, Applications and Future Directions

(Jiahui Geng & Zongxiong Chen et al., IJCAI 2023) :octocat: :book:

A Comprehensive Survey to Dataset Distillation

(Shiye Lei et al., TPAMI 2023) :octocat: :book:

Dataset Distillation: A Comprehensive Review

(Ruonan Yu & Songhua Liu et al., TPAMI 2023) :octocat: :book:

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions

(Ping Liu et al., 2025) :book:

Dataset Distillation in the Era of Large-Scale Data: Methods, Analysis, and Future Directions

(Xinyi Shang et al., 2025) :book:

Data-efficient Neural Network Training with Dataset Condensation

(Bo Zhao, The University of Edinburgh 2023) :book:

Generative Dataset Distillation

(Jovan Cicvarić, University of Tübingen 2023) :book:

Reducing Catastrophic Forgetting with Learning on Synthetic Data

(Wojciech Masarczyk et al., CVPR 2020 Workshop) :book:

Condensed Composite Memory Continual Learning

(Felix Wiewel et al., IJCNN 2021) :octocat: :book:

Distilled Replay: Overcoming Forgetting through Synthetic Samples

(Andrea Rosasco et al., IJCAI 2021 Workshop) :octocat: :book:

Sample Condensation in Online Continual Learning

(Mattia Sangermano et al., IJCNN 2022) :octocat: :book:

An Efficient Dataset Condensation Plugin and Its Application to Continual Learning

(Enneng Yang et al., NeurIPS 2023) :octocat: :book:

Summarizing Stream Data for Memory-Restricted Online Continual Learning

(Jianyang Gu et al., AAAI 2024) :octocat: :book:

CD2: Constrained Dataset Distillation for Few-Shot Class-Incremental Learning

(Kexin Bao et al., IJCAI 2025) :book:

Asymmetric Synthetic Data Update for Domain Incremental Dataset Distillation

(Minyoung Oh et al., ICLR 2026) :octocat: :book:

Privacy for Free: How does Dataset Condensation Help Privacy?

(Tian Dong et al., ICML 2022) :book:

Private Set Generation with Discriminative Information

(Dingfan Chen et al., NeurIPS 2022) :octocat: :book:

No Free Lunch in "Privacy for Free: How does Dataset Condensation Help Privacy"

(Nicholas Carlini et al., 2022) :book:

Backdoor Attacks Against Dataset Distillation

(Yugeng Liu et al., NDSS 2023) :octocat: :book:

Differentially Private Kernel Inducing Points (DP-KIP) for Privacy-preserving Data Distillation

(Margarita Vinaroz et al., 2023) :octocat: :book:

Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation

(Noel Loo et al., ICLR 2024) :book:

Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective

(Ming-Yu Chung et al., ICLR 2024) :book:

Differentially Private Dataset Condensation

(Zheng et al., NDSS 2024 Workshop) :book:

Adaptive Backdoor Attacks Against Dataset Distillation for Federated Learning

(Ze Chai et al., ICC 2024) :book:

Improving Noise Efficiency in Privacy-preserving Dataset Distillation

(Runkai Zheng et al., ICCV 2025) :octocat: :book:

SNEAKDOOR: Stealthy Backdoor Attacks against Distribution Matching-based Dataset Condensation

(He Yang & Dongyi Lv et al., NeurIPS 2025) :book:

Poisoned Distillation: Injecting Backdoors into Distilled Datasets Without Raw Data Access

(Ziyuan Yang et al., AAAI 2026) :book:

DP-GENG: Differentially Private Dataset Distillation Guided by DP-Generated Data

(Shuo Shi et al., AAAI 2026) :book:

Attention Hijacking: Backdooring Text Dataset Distillation via Semantic Anchors

(Hang Ren et al., ICML 2026) :book:

Soft-Label Anonymous Gastric X-ray Image Distillation

(Guang Li et al., ICIP 2020) :octocat: :book:

Compressed Gastric Image Generation Based on Soft-Label Dataset Distillation for Medical Data Sharing

(Guang Li et al., CMPB 2022) :octocat: :book:

Dataset Distillation for Medical Dataset Sharing

(Guang Li et al., AAAI 2023 Workshop) :octocat: :book:

Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation

(Yuchen Tian & Jiacheng Wang et al., MICCAI 2023 Workshop) :book:

Importance-Aware Adaptive Dataset Distillation

(Guang Li et al., NN 2024) :book:

Image Distillation for Safe Data Sharing in Histopathology

(Zhe Li et al., MICCAI 2024) :octocat: :book:

MedSynth: Leveraging Generative Model for Healthcare Data Sharing

(Renuga Kanagavelu et al., MICCAI 2024) :book:

Progressive Trajectory Matching for Medical Dataset Distillation

(Zhen Yu et al., 2024) :book:

Dataset Distillation in Medical Imaging: A Feasibility Study

(Muyang Li et al., 2024) :book:

Dataset Distillation for Histopathology Image Classification

(Cong Cong et al., 2024) :book:

Multi-modal Vision Pre-training for Medical Image Analysis

(Shaohao Rui & Lingzhi Chen et al., CVPR 2025) :octocat: :book:

FedWSIDD: Federated Whole Slide Image Classification via Dataset Distillation

(Haolong Jin et al., MICCAI 2025) :octocat: :book:

High-Order Progressive Trajectory Matching for Medical Image Dataset Distillation

(Le Dong et al., MICCAI 2025) :octocat: :book:

Low-Level Dataset Distillation for Medical Image Enhancemen

(Fengzhi Xu et al., 2025) :book:

Towards Data Quality-Aware Dataset Distillation in Bioimaging

(Bárbara Capelo et al., Bioimaging 2026) :book:

Federated Learning via Synthetic Data

(Jack Goetz et al., 2020) :book:

Distilled One-Shot Federated Learning

(Yanlin Zhou et al., 2020) :book:

DENSE: Data-Free One-Shot Federated Learning

(Jie Zhang & Chen Chen et al., NeurIPS 2022) :octocat: :book:

FedSynth: Gradient Compression via Synthetic Data in Federated Learning

(Shengyuan Hu et al., 2022) :book:

Meta Knowledge Condensation for Federated Learning

(Ping Liu et al., ICLR 2023) :book:

DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics

(Renjie Pi et al., CVPR 2023) :octocat: :book:

FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning

(Yuanhao Xiong & Ruochen Wang et al., CVPR 2023) :octocat: :book:

Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments

(Rui Song et al., IJCNN 2023) :octocat: :book:

FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations

(Hui-Po Wang et al., 2023) :octocat: :book:

Federated Learning on Virtual Heterogeneous Data with Local-global Distillation

(Chun-Yin Huang et al., TMLR 2024) :octocat: :book:

An Aggregation-Free Federated Learning for Tackling Data Heterogeneity

(Yuan Wang et al., CVPR 2024) :book:

Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchors

(Chun-Yin Huang et al., ICML 2024) :octocat: :book:

DCFL: Non-IID Awareness Dataset Condensation Aided Federated Learning

(Xingwang Wang et al., IJCNN 2024) :octocat: :book:

Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents

(Yuqi Jia & Saeed Vahidian et al., ECCV 2024) :octocat: :book:

One-Shot Collaborative Data Distillation

(William Holland et al., ECAI 2024) :octocat: :book:

FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical…

(Guochen Yan et al., AAAI 2025) :octocat: :book:

Graph Condensation for Graph Neural Networks

(Wei Jin et al., ICLR 2022) :octocat: :book:

Condensing Graphs via One-Step Gradient Matching

(Wei Jin et al., KDD 2022) :octocat: :book:

Graph Condensation via Receptive Field Distribution Matching

(Mengyang Liu et al., 2022) :book:

Kernel Ridge Regression-Based Graph Dataset Distillation

(Zhe Xu et al., KDD 2023) :octocat: :book:

Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data

(Xin Zheng et al., NeurIPS 2023) :octocat: :book:

Does Graph Distillation See Like Vision Dataset Counterpart?

(Beining Yang & Kai Wang et al., NeurIPS 2023) :octocat: :book:

CaT: Balanced Continual Graph Learning with Graph Condensation

(Yilun Liu et al., ICDM 2023) :octocat: :book:

Mirage: Model-Agnostic Graph Distillation for Graph Classification

(Mridul Gupta & Sahil Manchanda et al., ICLR 2024) :octocat: :book:

Graph Distillation with Eigenbasis Matching

(Yang Liu & Deyu Bo et al., ICML 2024) :octocat: :book:

Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching

(Yuchen Zhang & Tianle Zhang & Kai Wang et al., ICML 2024) :octocat: :book:

Graph Data Condensation via Self-expressive Graph Structure Reconstruction

(Zhanyu Liu & Chaolv Zeng et al., KDD 2024) :octocat: :book:

Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching

(Tianle Zhang & Yuchen Zhang & Kai Wang et al., 2024) :octocat: :book:

A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation

(Mohammad Hashemi et al., IJCAI 2024) :octocat: :book:

A Survey on Graph Condensation

(Hongjia Xu et al., 2024) :octocat: :book:

Graph Condensation: A Survey

(Xinyi Gao et al., TKDE 2025) :octocat: :book:

GC-Bench: An Open and Unified Benchmark for Graph Condensation

(Qingyun Sun & Ziying Chen et al., NeurIPS 2024) :octocat: :book:

GCondenser: Benchmarking Graph Condensation

(Yilun Liu et al., 2024) :octocat: :book:

GC-Bench: A Benchmark Framework for Graph Condensation with New Insights

(Shengbo Gong & Juntong Ni et al., 2024) :octocat: :book:

Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data

(Felipe Petroski Such et al., ICML 2020) :octocat: :book:

Learning to Generate Synthetic Training Data using Gradient Matching and Implicit Differentiation

(Dmitry Medvedev et al., AIST 2021) :octocat: :book:

Calibrated Dataset Condensation for Faster Hyperparameter Search

(Mucong Ding et al., 2024) :book:

Wearable ImageNet: Synthesizing Tileable Textures via Dataset Distillation

(George Cazenavette et al., CVPR 2022 Workshop) :globe_with_meridians: :octocat: :book:

Learning from Designers: Fashion Compatibility Analysis Via Dataset Distillation

(Yulan Chen et al., ICIP 2022) :book:

Galaxy Dataset Distillation with Self-Adaptive Trajectory Matching

(Haowen Guan et al., NeurIPS 2023 Workshop) :octocat: :book:

Infinite Recommendation Networks: A Data-Centric Approach

(Noveen Sachdeva et al., NeurIPS 2022) :octocat: :book:

Gradient Matching for Categorical Data Distillation in CTR Prediction

(Chen Wang et al., RecSys 2023) :book:

TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation

(Jiaqing Zhang et al., WWW 2025) :octocat: :book:

DIET: Learning to Distill Dataset Continually for Recommender Systems

(Jiaqing Zhang et al., 2026) :book:

FOSTER: First-order Dataset Distillation for Text-based Sequential Recommendation

(Hung Vinh Tran et al., arXiv 2026) :book:

Bidirectional Learning for Offline Infinite-width Model-based Optimization

(Can Chen et al., NeurIPS 2022) :octocat: :book:

Bidirectional Learning for Offline Model-based Biological Sequence Design

(Can Chen et al., ICML 2023) :octocat: :book:

Can We Achieve Robustness from Data Alone?

(Nikolaos Tsilivis et al., ICML 2022 Workshop) :book:

Towards Robust Dataset Learning

(Yihan Wu et al., 2022) :book:

Rethinking Data Distillation: Do Not Overlook Calibration

(Dongyao Zhu et al., ICCV 2023) :book:

Towards Trustworthy Dataset Distillation

(Shijie Ma et al., PR 2024) :octocat: :book:

Towards Adversarially Robust Dataset Distillation by Curvature Regularization

(Eric Xue et al., AAAI 2025) :globe_with_meridians: :octocat: :book:

Group Distributionally Robust Dataset Distillation with Risk Minimization

(Saeed Vahidian & Mingyu Wang & Jianyang Gu et al., ICLR 2025) :octocat: :book:

ROME is Forged in Adversity: Robust Distilled Datasets via Information Bottleneck

(Zheng Zhou et al., ICML 2025) :globe_with_meridians: :octocat: :book:

Mind Your Margin and Boundary: Are Your Distilled Datasets Truly Robust?

(Muquan Li et al., ICML 2026) :book:

Fair Graph Distillation

(Qizhang Feng et al., NeurIPS 2023) :book:

FairDD: Fair Dataset Distillation

(Qihang Zhou et al., NeurIPS 2025) :octocat: :book:

Fair Dataset Distillation via Cross-Group Barycenter Alignment

(Mohammad Hossein Moslemi et al., ICML 2026) :octocat: :book:

Data Distillation for Text Classification

(Yongqi Li et al., 2021) :book:

Dataset Distillation with Attention Labels for Fine-tuning BERT

(Aru Maekawa et al., ACL 2023) :octocat: :book:

DiLM: Distilling Dataset into Language Model for Text-level Dataset Distillation

(Aru Maekawa et al., NAACL 2024) :octocat: :book:

Textual Dataset Distillation via Language Model Embedding

(Yefan Tao et al., EMNLP 2024) :book:

UniDetox: Universal Detoxification of Large Language Models via Dataset Distillation

(Huimin Lu et al., ICLR 2025) :octocat: :book:

Knowledge Hierarchy Guided Biological-Medical Dataset Distillation for Domain LLM Training

(Xunxin Cai & Chengrui Wang & Qingqing Long et al., DASFAA 2025) :book:

Synthetic Text Generation for Training Large Language Models via Gradient Matching

(Dang Nguyen & Zeman Li et al., ICML 2025) :octocat: :book:

CondenseLM: LLMs-driven Text Dataset Condensation via Reward Matching

(Cheng Shen et al., EMNLP 2025) :octocat: :book:

Dancing with Still Images: Video Distillation via Static-Dynamic Disentanglement

(Ziyu Wang & Yue Xu et al., CVPR 2024) :octocat: :book:

Video Set Distillation: Information Diversification and Temporal Densifica

(Yinjie Zhao et al., 2024) :book:

A Large-Scale Study on Video Action Dataset Condensation

(Yang Chen et al., 2024) :octocat: :book:

Condensing Action Segmentation Datasets via Generative Network Inversion

(Guodong Ding et al., CVPR 2025) :book:

Latent Video Dataset Distillation

(Ning Li et al., CVPR 2025 Workshop) :octocat: :book:

Distill Video Datasets into Images

(Zhenghao Zhao et al., 2025) :book:

PRISM: Video Dataset Condensation with Progressive Refinement and Insertion for Sparse Motion

(Jaehyun Choi et al., CVPR 2026) :book:

Adaptive Latent Trajectory Anchoring for Action Segmentation Dataset Condensation

(Arthème Gauthier-Villars & Guodong Ding et al., ECCV 2026) :book:

New Properties of the Data Distillation Method When Working With Tabular Data

(Dmitry Medvedev et al., AIST 2020) :octocat: :book:

Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching

(Tao Feng & Jie Zhang et al., 2023) :book:

Multi-Source Domain Adaptation Meets Dataset Distillation through Dataset Dictionary Learning

(Eduardo Montesuma et al., ICASSP 2024) :book:

GSDD: Generative Space Dataset Distillation for Image Super-resolution

(Haiyu Zhang et al., AAAI 2024) :book:

Dataset Condensation for Time Series Classification via Dual Domain Matching

(Zhanyu Liu et al., KDD 2024) :octocat: :book:

CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting

(Jianrong Ding & Zhanyu Liu et al., NeurIPS 2024) :octocat: :book:

Less is More: Efficient Time Series Dataset Condensation via Two-fold Modal Matching

(Hao Miao et al., VLDB 2025) :octocat: :book:

DDTime: Dataset Distillation with Spectral Alignment and Information Bottleneck for Time-Series Forecasting

(Yuqi Li & Kuiye Ding et al., 2025) :book:

Harmonic Dataset Distillation for Time Series Forecasting

(Seungha Hong et al., AAAI 2026) :book:

Distilling Time Series Foundation Models for Efficient Forecasting

(Yuqi Li & Kuiye Ding et al., ICASSP 2026) :octocat: :book:

Effective Dataset Distillation for Spatio-Temporal Forecasting with Bi-dimensional Compression

(Taehyung Kwon & Yeonje Choi et al., ICDE 2026) :octocat: :book:

ShapeCond: Fast Shapelet-Guided Dataset Condensation for Time Series Classification

(Sijia Peng et al., 2026) :octocat: :book:

One Batch Is Enough: A Unified Dataset Condensation Framework for General Time Series Analysis

(Wei Shao et al., ICML 2026) :octocat: :book:

Dataset-Distillation Generative Model for Speech Emotion Recognition

(Fabian Ritter-Gutierrez et al., Interspeech 2024) :book:

Distilled Datamodel with Reverse Gradient Matching

(Jingwen Ye et al., CVPR 2024) :book:

Dataset Condensation Driven Machine Unlearning

(Junaid Iqbal Khan, 2024) :octocat: :book:

Behaviour Distillation

(Andrei Lupu et al., ICLR 2024) :octocat: :book:

Dataset Distillation for Offline Reinforcement Learning

(Jonathan Light & Yuanzhe Liu et al., ICML 2024 Workshop) :globe_with_meridians: :octocat: :book:

Offline Behavior Distillation

(Shiye Lei et al., NeurIPS 2024) :octocat: :book:

Distilling Reinforcement Learning into Single-Batch Datasets

(Connor Wilhelm et al., ECAI 2025) :book:

Algorithmic Guarantees for Distilling Supervised and Offline RL Datasets

(Aaryan Gupta et al., ICLR 2026) :book:

Distilling Long-tailed Datasets

(Zhenghao Zhao & Haoxuan Wang et al., CVPR 2025) :octocat: :book:

Rectifying Soft-Label Entangled Bias in Long-Tailed Dataset Distillation

(Chenyang Jiang et al., NeurIPS 2025) :octocat: :book:

Rethinking Long-tailed Dataset Distillation: A Uni-Level Framework with Unbiased Recovery and Relabeling

(Xiao Cui et al., AAAI 2026) :book:

Dataset Distillers Are Good Label Denoisers In the Wild

(Lechao Cheng et al., 2024) :octocat: :book:

Robust Dataset Condensation using Supervised Contrastive Learning

(Nicole Hee-Yeon Kim et al., ICCV 2025) :octocat: :book:

Fetch and Forge: Efficient Dataset Condensation for Object Detection

(Ding Qi et al., NeurIPS 2024) :book:

OD3: Optimization-free Dataset Distillation for Object Detection

(Salwa K. Al Khatib & Ahmed ElHagry & Shitong Shao et al., ICLR 2026) :octocat: :book:

Point Cloud Dataset Distillation

(Deyu Bo et al., ICML 2025) :book:

Dataset Distillation of 3D Point Clouds via Distribution Matching

(Jae-Young Yim & Dongwook Kim et al., NeurIPS 2025) :octocat: :book:

Parameterization-Based Dataset Distillation of 3D Point Clouds through Learnable Shape Morphing

(Dongwook Kim & Jae-Young Yim et al., ICLR 2026) :octocat: :book:

Towards Universal Dataset Distillation via Task-Driven Diffusion

(Ding Qi et al., CVPR 2025) :book:

Learning from Dense Events: Towards Fast Spiking Neural Networks Training via Event Dataset Distillation

(Shuhan Ye et al., 2025) :book:

EEG-DLite: Dataset Distillation for Efficient Large EEG Model Training

(Yuting Tang et al., AAAI 2026) :octocat: :book:

Secure and Explainable Fraud Detection in Finance via Hierarchical Multi-source Dataset Distillation

(Yiming Qian et al., ICAIFW 2025) :book:

ConceptCaps: a Distilled Concept Dataset for Interpretability in Music Models

(Bruno Sienkiewicz et al., 2026) :book:

Towards Realistic Remote Sensing Dataset Distillation with Discriminative Prototype-guided Diffusion

(Yonghao Xu et al., 2026) :book:

UniRain: Unified Image Deraining with RAG-based Dataset Distillation and Multi-objective Reweighted Optimization

(Qianfeng Yang et al., CVPR 2026) :octocat: :book:

FD2: A Dedicated Framework for Fine-Grained Dataset Distillation

(Hongxu Ma & Guang Li et al., ECCV 2026) :book:

Distill Once, Adapt Life-Long: Exploring Dataset Distillation for Continual Test-Time Adaptation

(Hyun-Kurl Jang & Jihun Kim & Hyeokjun Kweon et al., ECCV 2026) :octocat: :book:

D3S2: Diffusion-Guided Dataset Distillation for Semantic Segmentation

(Wenjie Zheng et al., arXiv 2026) :book:

Beginning of Awesome Dataset Distillation

Most Popular AI Research Aug 2022

一个项目帮你了解数据集蒸馏Dataset Distillation

浓缩就是精华：用大一统视角看待数据集蒸馏