Dataset Condensation
23 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
Dataset Condensation with Gradient Matching
VICO-UoE/DatasetCondensation
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ICLR 2021
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
VICO-UoE/DatasetCondensation
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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
vico-uoe/it-gan
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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.
M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy
Nowadays, optimization-oriented methods have been the primary method in the field of dataset condensation for achieving SOTA results.
Dataset Condensation with Differentiable Siamese Augmentation
VICO-UoE/DatasetCondensation
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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
saehyung-lee/dcc
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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
kaiwang960112/cafe
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CVPR 2022
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
snu-mllab/efficient-dataset-condensation
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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.
