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NUS-HPC-AI-Lab avatar

NUS-HPC-AI-Lab/EDF

0
View on GitHub↗
23 stars·1 fork·Python·6 views

EDF

In this work, we propose to emphasize discriminative features for dataset distillation in the complex scenario, i.e. images in complex scenarios are characterized by significant variations in object sizes and the presence of a large amount of class-irrelevant information.

Features

  • Gradient Trajectory Matching - Emphasizes discriminative features for distillation in complex scenarios.

Star history

Star history chart for nus-hpc-ai-lab/edfStar history chart for nus-hpc-ai-lab/edf

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.

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Projects sharing features with EDF

These projects share indexed features with EDF. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • georgecazenavette/mtt-distillationgeorgecazenavette avatar

    georgecazenavette/mtt-distillation

    440View on GitHub↗

    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.

    Python
    View on GitHub↗440
  • gzyaftermath/datmGzyAftermath avatar

    GzyAftermath/DATM

    0View on GitHub↗

    Code

    View on GitHub↗0
  • justincui03/teslajustincui03 avatar

    justincui03/tesla

    30View on GitHub↗

    Hello!!! Thanks for checking out our repo and paper! 🍻

    Python
    View on GitHub↗30
  • angusdujw/ftd-distillationAngusDujw avatar

    AngusDujw/FTD-distillation

    40View on GitHub↗

    This repo contains code for training expert trajectories and distilling synthetic data from our Dataset Distillation by FTD paper (CVPR 2023).

    Python
    View on GitHub↗40
Compare all 13 related projects→

Frequently asked questions

What does nus-hpc-ai-lab/edf do?

In this work, we propose to emphasize discriminative features for dataset distillation in the complex scenario, i.e. images in complex scenarios are characterized by significant variations in object sizes and the presence of a large amount of class-irrelevant information.

What are the main features of nus-hpc-ai-lab/edf?

The main features of nus-hpc-ai-lab/edf are: Gradient Trajectory Matching.

Which projects share features with nus-hpc-ai-lab/edf?

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/pad — Matching-based Dataset Distillation methods can be summarized into two steps:. angusdujw/ftd-distillation — This repo contains code for training expert trajectories and distilling synthetic data from our Dataset Distillation…