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Dataset Condensation

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논문

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Diffusion Dataset Condensation: Training Your Diffusion Model Faster with Less Data

Video Dataset Condensation with Diffusion Models

SCORE: Soft Label Compression-Centric Dataset Condensation via Coding Rate Optimization

Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation

A Large-Scale Study on Video Action Dataset Condensation

Bi-Directional Multi-Scale Graph Dataset Condensation via Information Bottleneck

Decomposed Distribution Matching in Dataset Condensation

Diffusion-Augmented Coreset Expansion for Scalable Dataset Distillation

Towards Model-Agnostic Dataset Condensation by Heterogeneous Models

Dataset Condensation with Latent Quantile Matching

Mitigating Bias in Dataset Distillation

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

DANCE: Dual-View Distribution Alignment for Dataset Condensation

Spectral Greedy Coresets for Graph Neural Networks

Calibrated Dataset Condensation for Faster Hyperparameter Search

Koopcon: A new approach towards smarter and less complex learning

Elucidating the Design Space of Dataset Condensation

Distilling Datasets Into Less Than One Image

Dataset Condensation for Time Series Classification via Dual Domain Matching

Multisize Dataset Condensation

Connect the dots: Dataset Condensation, Differential Privacy, and Adversarial Uncertainty

Is Adversarial Training with Compressed Datasets Effective?

Dataset Condensation Driven Machine Unlearning

M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy

DCFL: Non-IID awareness Data Condensation aided Federated Learning

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

You Only Condense Once: Two Rules for Pruning Condensed Datasets

Fast Graph Condensation with Structure-based Neural Tangent Kernel

TF-DCon: Leveraging Large Language Models (LLMs) to Empower Training-Free Dataset Condensation for Content-Based Recommendation

Leveraging Hierarchical Feature Sharing for Efficient Dataset Condensation

Dataset Condensation for Recommendation

Dataset Condensation via Generative Model

Improved Distribution Matching for Dataset Condensation

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

Revisiting Permutation Symmetry for Merging Models between Different Datasets

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

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.