Dataset distillation is the task of synthesizing a small dataset such that models trained on it achieve high performance on the original large dataset. A ...
Nov 27, 2018 · Abstract:Model distillation aims to distill the knowledge of a complex model into a simpler one. In this paper, we consider an alternative ...
May 24, 2023 · Dataset distillation is a technique in machine learning that concerns selecting a representative subset of data samples from a larger dataset ...
Dataset distillation is the task of synthesizing a small dataset such that a model trained on the synthetic set will match the test accuracy of the model ...
People also ask
What is dataset distillation?
What does it mean to distill data?
What is dataset distillation learning github?
What is dataset distillation learning ICML?
Oct 7, 2023 · Dataset distillation (DD) aims distilling knowledge of the dataset into some synthetic data while preserving the per- formance of the models ...
Dataset distillation is the task of synthesizing a small dataset such that models trained on it achieve high performance on the original large dataset.
Aug 7, 2022 · Dataset distillation is the task of synthesizing a small dataset such that models trained on it achieve high performance on the original large ...
In the image classification task, the typical method of dataset distillation is to distill the information of the dataset into a few synthetic images with the ...
We provide a PyTorch implementation of Dataset Distillation. We distill the knowledge of tens of thousands of images into a few synthetic training images called ...
Dataset distillation (DD) aims to synthesize a small dataset whose test performance is comparable to a full dataset using the same model.