Main
-
Early Work
-
Gradient/Trajectory Matching Surrogate Objective
-
Distribution/Feature Matching Surrogate Objective
-
Dataset Distillation via the Wasserstein Metric
-
Dataset Condensation with Latent Quantile Matching
-
Kernel-Based Distillation
-
Provable and Efficient Dataset Distillation for Kernel Ridge Regression
-
Distilled Dataset Parametrization
-
Dataset Condensation with Latent Space Knowledge Factorization and Sharing
-
Slimmable Dataset Condensation
-
Few-Shot Dataset Distillation via Translative Pre-Training
-
MGDD: A Meta Generator for Fast Dataset Distillation
-
Leveraging Hierarchical Feature Sharing for Efficient Dataset Condensation
-
Generative Distillation
-
Dataset Condensation via Generative Model
-
Generative Dataset Distillation: Balancing Global Structure and Local Details
-
Data-to-Model Distillation: Data-Efficient Learning Framework
-
Latent Dataset Distillation with Diffusion Models
-
Better Optimization
-
MIM4DD: Mutual Information Maximization for Dataset Distillation
-
Dataset Distillation in Latent Space
-
Teddy: Efficient Large-Scale Dataset Distillation via Taylor-Approximated Matching
-
Better Understanding
-
On Implicit Bias in Overparameterized Bilevel Optimization
-
On the Size and Approximation Error of Distilled Sets
-
A Theoretical Study of Dataset Distillation
-
Mitigating Bias in Dataset Distillation
-
Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learning
-
Not All Samples Should Be Utilized Equally: Towards Understanding and Improving Dataset Distillation
-
Label Distillation
-
Label-Augmented Dataset Distillation
-
DRUPI: Dataset Reduction Using Privileged Information
-
Dataset Quantization
-
Decoupled Distillation
-
Information Compensation: A Fix for Any-scale Dataset Distillation
-
Curriculum Dataset Distillation
-
Multimodal Distillation
-
Self-Supervised Distillation
-
Object Detection
-
Fetch and Forge: Efficient Dataset Condensation for Object Detection
-
Benchmark
-
A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness
-
Survey
-
Data Distillation: A Survey
-
Ph.D. Thesis
-
Data-efficient Neural Network Training with Dataset Condensation
-
Workshop
-
1st CVPR Workshop on Dataset Distillation
-
Challenge
Applications
-
Continual Learning
-
Reducing Catastrophic Forgetting with Learning on Synthetic Data
-
Privacy
-
Privacy for Free: How does Dataset Condensation Help Privacy?
-
No Free Lunch in "Privacy for Free: How does Dataset Condensation Help Privacy"
-
Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation
-
Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective
-
Differentially Private Dataset Condensation
-
Adaptive Backdoor Attacks Against Dataset Distillation for Federated Learning
-
Medical
-
Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
-
Importance-Aware Adaptive Dataset Distillation
-
MedSynth: Leveraging Generative Model for Healthcare Data Sharing
-
Progressive Trajectory Matching for Medical Dataset Distillation
-
Dataset Distillation in Medical Imaging: A Feasibility Study
-
Dataset Distillation for Histopathology Image Classification
-
Federated Learning
-
Federated Learning via Synthetic Data
-
Distilled One-Shot Federated Learning
-
FedSynth: Gradient Compression via Synthetic Data in Federated Learning
-
Meta Knowledge Condensation for Federated Learning
-
Federated Virtual Learning on Heterogeneous Data with Local-global Distillation
-
An Aggregation-Free Federated Learning for Tackling Data Heterogeneity
-
DCFL: Non-IID Awareness Dataset Condensation Aided Federated Learning
-
Graph Neural Network
-
No further updates will be made regarding graph distillation topics as sufficient papers and summary projects are already available on the subject
-
Graph Condensation via Receptive Field Distribution Matching
-
Fair Graph Distillation
-
Graph Neural Network - Survey
-
Graph Neural Network - Benchmark
-
Neural Architecture Search
-
Calibrated Dataset Condensation for Faster Hyperparameter Search
-
Fashion, Art, and Design
-
Learning from Designers: Fashion Compatibility Analysis Via Dataset Distillation
-
Recommender Systems
-
Gradient Matching for Categorical Data Distillation in CTR Prediction
-
Blackbox Optimization
-
Trustworthy
-
Can We Achieve Robustness from Data Alone?
-
Towards Robust Dataset Learning
-
Rethinking Data Distillation: Do Not Overlook Calibration
-
Towards Trustworthy Dataset Distillation
-
Towards Adversarially Robust Dataset Distillation by Curvature Regularization
-
Text
-
Data Distillation for Text Classification
-
Textual Dataset Distillation via Language Model Embedding
-
Tabular
-
Retrieval
-
Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching
-
Video
-
Video Set Distillation: Information Diversification and Temporal Densifica
-
Domain Adaptation
-
Multi-Source Domain Adaptation Meets Dataset Distillation through Dataset Dictionary Learning
-
Super Resolution
-
GSDD: Generative Space Dataset Distillation for Image Super-resolution
-
Time Series
-
Speech
-
Dataset-Distillation Generative Model for Speech Emotion Recognition
-
Machine Unlearning
-
Distilled Datamodel with Reverse Gradient Matching
-
Reinforcement Learning
-
Long-Tail
-
Distilling Long-tailed Datasets
-
Learning with Noisy Labels