How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
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.
The main features of georgecazenavette/mtt-distillation are: Gradient Trajectory Matching, Other Applications.
Projects with overlapping indexed features include: dm-medvedev/dataset-distillation — The code was forked from the initial project and changed by Dmitry Medvedev. This project contains code of experiments… ggchen1997/bdi — We propose BiDirectional learning for offline Infinite-width model-based optimization (BDI) between the high-scoring… ggchen1997/bib-icml2023-submission — We propose BIB: BIdirectional Learning for Offline Model-based Biological Sequence Design, which focuses on designing… gzyaftermath/datm — Code. haowenguan/galaxy-dataset-distillation — This is the official repository for paper Discovering Galaxy Features via Dataset Distillation. Our work contains the… angusdujw/ftd-distillation — This repo contains code for training expert trajectories and distilling synthetic data from our Dataset Distillation…
The code was forked from the initial project and changed by Dmitry Medvedev. This project contains code of experiments for coursework
We propose BiDirectional learning for offline Infinite-width model-based optimization (BDI) between the high-scoring designs and the static dataset (a.k.a. low-scoring designs).
We propose BIB: BIdirectional Learning for Offline Model-based Biological Sequence Design, which focuses on designing biological sequences to maximize some sequence score.
This repo contains code for training expert trajectories and distilling synthetic data from our Dataset Distillation by FTD paper (CVPR 2023).