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VILA-Lab/SRe2L

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SRe2L

This is a collection of our work targeted at large-scale dataset distillation.

Features

  • Decoupled Distillation - Condenses datasets at scale by decoupling training and synthesis.
  • Self-Supervised Distillation - Compresses datasets using self-supervised learning principles.

Star history

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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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Frequently asked questions

What does vila-lab/sre2l do?

This is a collection of our work targeted at large-scale dataset distillation.

What are the main features of vila-lab/sre2l?

The main features of vila-lab/sre2l are: Decoupled Distillation, Self-Supervised Distillation.

Which projects share features with vila-lab/sre2l?

Projects with overlapping indexed features include: becarefulofyournaoke/bps — This repo contains PyTorch implementation of Balanced Dataset Distillation via Modeling Multiple Visual Pattern… brian-moser/prism — Official PyTorch implementation of paper (TMLR'26) PRISM : Diversifying Dataset Distillation by Decoupling… dil-iith/dire — Code for WACV 2026 paper "DiRe: Diversity-promoting Regularization for Dataset Condensation". georgecazenavette/linear-gradient-matching — Project Page | arXiv. jiacheng8/cv-dd — Jiacheng Cui, Zhaoyi Li, Xiaochen Ma, Xinyue Bi, Yaxin Luo, Zhiqiang Shen. angusdujw/diversity-driven-synthesis — The sharp increase in data-related expenses has motivated research into condensing datasets while retaining the most…

Projects sharing features with SRe2L

These projects share indexed features with SRe2L. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • becarefulofyournaoke/bpsB

    BeCarefulOfYournaoke/BPS

    0View on GitHub↗

    This repo contains PyTorch implementation of Balanced Dataset Distillation via Modeling Multiple Visual Pattern Distribution (BPS, CVPR 2026). BPS produces a pattern-balanced condensed dataset via modeling the multiple visual pattern distribution within each class. This repo contains code for…

    View on GitHub↗0
  • brian-moser/prismBrian-Moser avatar

    Brian-Moser/prism

    1View on GitHub↗

    Official PyTorch implementation of paper (TMLR'26) PRISM : Diversifying Dataset Distillation by Decoupling Architectural Priors.

    Python
    View on GitHub↗1
  • dil-iith/direDIL-IITH avatar

    DIL-IITH/DiRe

    1View on GitHub↗

    Code for WACV 2026 paper "DiRe: Diversity-promoting Regularization for Dataset Condensation"

    Python
    View on GitHub↗1
  • angusdujw/diversity-driven-synthesisAngusDujw avatar

    AngusDujw/Diversity-Driven-Synthesis

    8View on GitHub↗

    The sharp increase in data-related expenses has motivated research into condensing datasets while retaining the most informative features. Dataset distillation has thus recently come to the fore. This paradigm generates synthetic datasets that are representative enough to replace the original…

    Python
    View on GitHub↗8
Compare all 15 related projects→