Dataset Condensation

12 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.

Privacy for Free: How does Dataset Condensation Help Privacy?

Guang000/Awesome-Dataset-Distillation 1 Jun 2022

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.