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This repo contains code for training expert trajectories and distilling synthetic data from our Dataset Distillation by Matching Training Trajectories paper (CVPR 2022). Please see our project page for more results.
This repo contains code for training expert trajectories and distilling synthetic data from our Dataset Distillation by FTD paper (CVPR 2023).
Dataset condensation aims to condense a large training set T into a small synthetic set S such that the model trained on the small synthetic set can obtain comparable testing performance to that trained on the large training set.
The main features of vico-uoe/datasetcondensation are: Gradient Trajectory Matching.
Projects with overlapping indexed features include: georgecazenavette/mtt-distillation — This repo contains code for training expert trajectories and distilling synthetic data from our Dataset Distillation… gzyaftermath/datm — Code. justincui03/tesla — Hello!!! Thanks for checking out our repo and paper! 🍻. nialiu/att — This repository contains code for training expert trajectories and distilling synthetic data for the paper: Dataset… nus-hpc-ai-lab/edf — In this work, we propose to emphasize discriminative features for dataset distillation in the complex scenario, i.e.… angusdujw/ftd-distillation — This repo contains code for training expert trajectories and distilling synthetic data from our Dataset Distillation…
