Dataset Condensation
24 papers with code • 0 benchmarks • 0 datasets
Condense the full dataset into a tiny set of synthetic data.
Benchmarks
These leaderboards are used to track progress in Dataset Condensation
Most implemented papers
Efficient Dataset Distillation Using Random Feature Approximation
yolky/rfad
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Dataset distillation compresses large datasets into smaller synthetic coresets which retain performance with the aim of reducing the storage and computational burden of processing the entire dataset.
Improved Distribution Matching for Dataset Condensation
uitrbn/idm
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CVPR 2023
In this paper, we propose a novel dataset condensation method based on distribution matching, which is more efficient and promising.
Privacy for Free: How does Dataset Condensation Help Privacy?
In this work, we for the first time identify that dataset condensation (DC) which is originally designed for improving training efficiency is also a better solution to replace the traditional data generators for private data generation, thus providing privacy for free.
Delving into Effective Gradient Matching for Dataset Condensation
In this work, we delve into the gradient matching method from a comprehensive perspective and answer the critical questions of what, how, and where to match.
Dataset Condensation with Latent Space Knowledge Factorization and Sharing
In this paper, we introduce a novel approach for systematically solving dataset condensation problem in an efficient manner by exploiting the regularity in a given dataset.
Dataset Distillation: A Comprehensive Review
Recent success of deep learning is largely attributed to the sheer amount of data used for training deep neural networks. Despite the unprecedented success, the massive data, unfortunately, significantly increases the burden on storage and transmission and further gives rise to a cumbersome model training process.
Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching
The expenses involved in training state-of-the-art deep hashing retrieval models have witnessed an increase due to the adoption of more sophisticated models and large-scale datasets.
Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective
VILA-Lab/SRe2L
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NeurIPS 2023
The proposed method demonstrates flexibility across diverse dataset scales and exhibits multiple advantages in terms of arbitrary resolutions of synthesized images, low training cost and memory consumption with high-resolution synthesis, and the ability to scale up to arbitrary evaluation network architectures.
Fast Graph Condensation with Structure-based Neural Tangent Kernel
The rapid development of Internet technology has given rise to a vast amount of graph-structured data.
You Only Condense Once: Two Rules for Pruning Condensed Datasets
he-y/you-only-condense-once
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NeurIPS 2023
However, these scenarios have two significant challenges: 1) the varying computational resources available on the devices require a dataset size different from the pre-defined condensed dataset, and 2) the limited computational resources often preclude the possibility of conducting additional condensation processes.
