Dataset Condensation

18 papers with code • 0 benchmarks • 0 datasets

Condense the full dataset into a tiny set of synthetic data.

Most implemented papers

Dataset Condensation with Gradient Matching

VICO-UoE/DatasetCondensation 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.

Synthesizing Informative Training Samples with GAN

vico-uoe/it-gan 15 Apr 2022

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

amazon-research/doscond 15 Jun 2022

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.

Dataset Condensation with Differentiable Siamese Augmentation

VICO-UoE/DatasetCondensation 16 Feb 2021

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 Distribution Matching

VICO-UoE/DatasetCondensation 8 Oct 2021

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.

Dataset Condensation with Contrastive Signals

saehyung-lee/dcc 7 Feb 2022

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 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 30 May 2022

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

justincui03/dc_benchmark 20 Jul 2022

Dataset Condensation is a newly emerging technique aiming at learning a tiny dataset that captures the rich information encoded in the original dataset.

Efficient Dataset Distillation Using Random Feature Approximation

yolky/rfad 21 Oct 2022

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.