Dataset Condensation
33 papers with code • 0 benchmarks • 0 datasets
Condense the full dataset into a tiny set of synthetic data.
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Libraries
Use these libraries to find Dataset Condensation models and implementationsMost implemented papers
Dataset Condensation with Gradient Matching
As the state-of-the-art machine learning methods in many fields rely on larger datasets, storing datasets and training models on them become significantly more expensive.
Dataset Condensation with Distribution Matching
Computational cost of training state-of-the-art deep models in many learning problems is rapidly increasing due to more sophisticated models and larger datasets.
Synthesizing Informative Training Samples with GAN
However, traditional GANs generated images are not as informative as the real training samples when being used to train deep neural networks.
Condensing Graphs via One-Step Gradient Matching
However, existing approaches have their inherent limitations: (1) they are not directly applicable to graphs where the data is discrete; and (2) the condensation process is computationally expensive due to the involved nested optimization.
Generalized Large-Scale Data Condensation via Various Backbone and Statistical Matching
We call this perspective "generalized matching" and propose Generalized Various Backbone and Statistical Matching (G-VBSM) in this work, which aims to create a synthetic dataset with densities, ensuring consistency with the complete dataset across various backbones, layers, and statistics.
Dataset Condensation with Differentiable Siamese Augmentation
In many machine learning problems, large-scale datasets have become the de-facto standard to train state-of-the-art deep networks at the price of heavy computation load.
Dataset Condensation with Contrastive Signals
However, in this study, we prove that the existing DC methods can perform worse than the random selection method when task-irrelevant information forms a significant part of the training dataset.
CAFE: Learning to Condense Dataset by Aligning Features
Dataset condensation aims at reducing the network training effort through condensing a cumbersome training set into a compact synthetic one.
Dataset Condensation via Efficient Synthetic-Data Parameterization
The great success of machine learning with massive amounts of data comes at a price of huge computation costs and storage for training and tuning.
DC-BENCH: Dataset Condensation Benchmark
Dataset Condensation is a newly emerging technique aiming at learning a tiny dataset that captures the rich information encoded in the original dataset.