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This is the official repository for paper Discovering Galaxy Features via Dataset Distillation. Our work contains the experiment code for galaxy dataset distillation and Self-Adaptive Trajectory Matching (STM) algorithm, an improved version of Matching Training Trajectory (MTT).
The main features of haowenguan/galaxy-dataset-distillation are: 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… georgecazenavette/mtt-distillation — This repo contains code for training expert trajectories and distilling synthetic data from our Dataset Distillation… 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… ichbill/ltdd — Existing DD methods exhibit degraded performance when applied to imbalanced datasets, especially when the imbalance… mcg-nju/video-dc — The official implementation of A Large-Scale Study on Video Action Dataset Condensation.
The code was forked from the initial project and changed by Dmitry Medvedev. This project contains code of experiments for coursework
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.
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.