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Dataset Distillation

Dataset distillation is the task of synthesizing a small dataset such that models trained on it achieve high performance on the original large dataset. A dataset distillation algorithm takes as input a large real dataset to be distilled (training set), and outputs a small synthetic distilled dataset, which is evaluated via testing models trained on this distilled dataset on a separate real dataset (validation/test set). A good small distilled dataset is not only useful in dataset understanding, but has various applications (e.g., continual learning, privacy, neural architecture search, etc.).

Papers

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Showing 151–200 of 216 papers

TitleStatusHype
Hierarchical Features Matter: A Deep Exploration of GAN Priors for Improved Dataset Distillation—0
Mitigating Bias in Dataset Distillation—0
BACON: Bayesian Optimal Condensation Framework for Dataset DistillationCode0
SelMatch: Effectively Scaling Up Dataset Distillation via Selection-Based Initialization and Partial Updates by Trajectory Matching—0
Curriculum Dataset Distillation—0
ATOM: Attention Mixer for Efficient Dataset DistillationCode0
Practical Dataset Distillation Based on Deep Support Vectors—0
Let's Focus: Focused Backdoor Attack against Federated Transfer Learning—0
Generative Dataset Distillation: Balancing Global Structure and Local Details—0
Deep Support Vectors—0
Exploring the potential of prototype-based soft-labels data distillation for imbalanced data classification—0
Exploring the Impact of Dataset Bias on Dataset DistillationCode0
Progressive trajectory matching for medical dataset distillation—0
DD-RobustBench: An Adversarial Robustness Benchmark for Dataset DistillationCode0
Towards Adversarially Robust Dataset Distillation by Curvature RegularizationCode0
One Category One Prompt: Dataset Distillation using Diffusion Models—0
Latent Dataset Distillation with Diffusion Models—0
Distributional Dataset Distillation with Subtask DecompositionCode0
Importance-Aware Adaptive Dataset Distillation—0
MIM4DD: Mutual Information Maximization for Dataset Distillation—0
Dataset Distillation via Adversarial Prediction MatchingCode0
Boosting the Cross-Architecture Generalization of Dataset Distillation through an Empirical StudyCode0
Dataset Distillation via the Wasserstein Metric—0
Discovering Galaxy Features via Dataset DistillationCode0
Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective—0
Dataset Distillation in Latent Space—0
QuickDrop: Efficient Federated Unlearning by Integrated Dataset Distillation—0
Sequential Subset Matching for Dataset DistillationCode0
Linear Mode Connectivity in Sparse Neural Networks—0
AST: Effective Dataset Distillation through Alignment with Smooth and High-Quality Expert TrajectoriesCode0
Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality—0
FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks—0
Multi-Source Domain Adaptation meets Dataset Distillation through Dataset Dictionary Learning—0
Towards Mitigating Architecture Overfitting on Distilled DatasetsCode0
Exploring Multilingual Text Data DistillationCode0
Rethinking Data Distillation: Do Not Overlook Calibration—0
Dataset Distillation Meets Provable Subset Selection—0
Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching—0
On the Size and Approximation Error of Distilled Sets—0
A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness—0
A Survey on Dataset Distillation: Approaches, Applications and Future Directions—0
Federated Virtual Learning on Heterogeneous Data with Local-global Distillation—0
Evaluating the effect of data augmentation and BALD heuristics on distillation of Semantic-KITTI dataset—0
Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles—0
Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation—0
Dataset Distillation: A Comprehensive Review—0
A Comprehensive Survey of Dataset Distillation—0
Slimmable Dataset Condensation—0
Few-Shot Dataset Distillation via Translative Pre-Training—0
On Implicit Bias in Overparameterized Bilevel Optimization—0
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