SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–100 of 216 papers

TitleStatusHype
Vision-Language Dataset DistillationCode1
CaO_2: Rectifying Inconsistencies in Diffusion-Based Dataset DistillationCode1
DiLM: Distilling Dataset into Language Model for Text-level Dataset DistillationCode1
Can pre-trained models assist in dataset distillation?Code1
Scaling Up Dataset Distillation to ImageNet-1K with Constant MemoryCode1
What is Dataset Distillation Learning?Code1
D^4M: Dataset Distillation via Disentangled Diffusion ModelCode1
A Large-Scale Study on Video Action Dataset CondensationCode1
Dataset Distillation via Vision-Language Category PrototypeCode1
Dataset Distillation with Convexified Implicit GradientsCode1
Dataset DistillationCode1
Efficient Dataset Distillation Using Random Feature ApproximationCode1
DataDAM: Efficient Dataset Distillation with Attention MatchingCode1
Emphasizing Discriminative Features for Dataset Distillation in Complex ScenariosCode1
Dataset Factorization for CondensationCode1
Low-Rank Similarity Mining for Multimodal Dataset DistillationCode1
Dark Distillation: Backdooring Distilled Datasets without Accessing Raw Data—0
Information-Guided Diffusion Sampling for Dataset Distillation—0
Knowledge Distillation and Dataset Distillation of Large Language Models: Emerging Trends, Challenges, and Future Directions—0
Dataset Distillation via the Wasserstein Metric—0
Knowledge Hierarchy Guided Biological-Medical Dataset Distillation for Domain LLM Training—0
Hierarchical Features Matter: A Deep Exploration of GAN Priors for Improved Dataset Distillation—0
Curriculum Dataset Distillation—0
Hierarchical Features Matter: A Deep Exploration of Progressive Parameterization Method for Dataset Distillation—0
Dataset Distillation Meets Provable Subset Selection—0
Heavy Labels Out! Dataset Distillation with Label Space Lightening—0
Image Dataset Compression Based on Matrix Product States—0
Dataset Distillation in Medical Imaging: A Feasibility Study—0
Dataset Distillation in Latent Space—0
Contrastive Learning-Enhanced Trajectory Matching for Small-Scale Dataset Distillation—0
Efficient Dataset Distillation via Diffusion-Driven Patch Selection for Improved Generalization—0
Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learning—0
Label-Augmented Dataset Distillation—0
Dataset Distillation for Quantum Neural Networks—0
FYI: Flip Your Images for Dataset Distillation—0
Privacy-Preserving Federated Learning via Dataset Distillation—0
Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory—0
Distribution-aware Dataset Distillation for Efficient Image Restoration—0
Finding Stable Subnetworks at Initialization with Dataset Distillation—0
FocusDD: Real-World Scene Infusion for Robust Dataset Distillation—0
Dataset Distillation for Histopathology Image Classification—0
Distilling Desired Comments for Enhanced Code Review with Large Language Models—0
Class-Imbalanced-Aware Adaptive Dataset Distillation for Scalable Pretrained Model on Credit Scoring—0
FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks—0
Adaptive Dataset Quantization—0
FairDD: Fair Dataset Distillation via Synchronized Matching—0
FedWSIDD: Federated Whole Slide Image Classification via Dataset Distillation—0
Generative Dataset Distillation Based on Self-knowledge Distillation—0
Dataset Distillation-based Hybrid Federated Learning on Non-IID Data—0
Diffusion-Augmented Coreset Expansion for Scalable Dataset Distillation—0
Show:102550
← PrevPage 2 of 5Next →

No leaderboard results yet.