12 papers with code • 0 benchmarks • 0 datasets
Condense the full dataset into a tiny set of synthetic data.
These leaderboards are used to track progress in Dataset Condensation
However, traditional GANs generated images are not as informative as the real training samples when being used to train deep neural networks.
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.
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 aims at reducing the network training effort through condensing a cumbersome training set into a compact synthetic one.
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.
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.