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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
Let's Focus: Focused Backdoor Attack against Federated Transfer Learning—0
Leveraging Multi-Modal Information to Enhance Dataset Distillation—0
LiDAR dataset distillation within bayesian active learning framework: Understanding the effect of data augmentation—0
Linear Mode Connectivity in Sparse Neural Networks—0
Dataset Distillation for Quantum Neural Networks—0
MDM: Advancing Multi-Domain Distribution Matching for Automatic Modulation Recognition Dataset Synthesis—0
MetaDD: Boosting Dataset Distillation with Neural Network Architecture-Invariant Generalization—0
MGD^3: Mode-Guided Dataset Distillation using Diffusion Models—0
Video Set Distillation: Information Diversification and Temporal Densification—0
Dataset Distillation for Histopathology Image Classification—0
Dataset Distillation-based Hybrid Federated Learning on Non-IID Data—0
Multi-Source Domain Adaptation meets Dataset Distillation through Dataset Dictionary Learning—0
Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles—0
Towards Stable and Storage-efficient Dataset Distillation: Matching Convexified Trajectory—0
Not All Samples Should Be Utilized Equally: Towards Understanding and Improving Dataset Distillation—0
Dataset Distillation as Pushforward Optimal Quantization—0
Omni-supervised Facial Expression Recognition via Distilled Data—0
Adaptive Dataset Quantization—0
One Category One Prompt: Dataset Distillation using Diffusion Models—0
On Implicit Bias in Overparameterized Bilevel Optimization—0
On Learning Representations for Tabular Data Distillation—0
Data-Efficient Generation for Dataset Distillation—0
On the Size and Approximation Error of Distilled Sets—0
OPTICAL: Leveraging Optimal Transport for Contribution Allocation in Dataset Distillation—0
PCPs: Patient Cardiac Prototypes—0
Permutation-Invariant and Orientation-Aware Dataset Distillation for 3D Point Clouds—0
Practical Dataset Distillation Based on Deep Support Vectors—0
Primitive3D: 3D Object Dataset Synthesis from Randomly Assembled Primitives—0
Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning—0
Progressive trajectory matching for medical dataset distillation—0
QuickDrop: Efficient Federated Unlearning by Integrated Dataset Distillation—0
Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality—0
Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective—0
Towards Universal Dataset Distillation via Task-Driven Diffusion—0
Trust-Aware Diversion for Data-Effective Distillation—0
Robust Dataset Distillation by Matching Adversarial Trajectories—0
Robust Offline Reinforcement Learning for Non-Markovian Decision Processes—0
Dark Distillation: Backdooring Distilled Datasets without Accessing Raw Data—0
Secure Federated Data Distillation—0
Curriculum Dataset Distillation—0
Contrastive Learning-Enhanced Trajectory Matching for Small-Scale Dataset Distillation—0
SelMatch: Effectively Scaling Up Dataset Distillation via Selection-Based Initialization and Partial Updates by Trajectory Matching—0
Efficient Low-Resolution Face Recognition via Bridge Distillation—0
UDD: Dataset Distillation via Mining Underutilized Regions—0
Slimmable Dataset Condensation—0
Evaluating the effect of data augmentation and BALD heuristics on distillation of Semantic-KITTI dataset—0
Efficient Dataset Distillation via Diffusion-Driven Patch Selection for Improved Generalization—0
Class-Imbalanced-Aware Adaptive Dataset Distillation for Scalable Pretrained Model on Credit Scoring—0
Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator—0
Understanding Dataset Distillation via Spectral Filtering—0
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