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Back to justincui03/tesla

Projects sharing features with Tesla

13 open-source projects similar to justincui03/tesla, ranked by shared indexed features. Tags may describe platforms or build tools rather than the same primary purpose. Check each project’s use case, license, and deployment requirements before treating it as a replacement.

  • 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
  • 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
  • nialiu/attNiaLiu avatar

    NiaLiu/ATT

    9View on GitHub↗

    This repository contains code for training expert trajectories and distilling synthetic data for the paper: Dataset Distillation by Automatic Training Trajectories. The listed is the steps to run the code. 1. Set up enveriments. 2. Create an wandb account for monitoring distillation process…

    Python
    View on GitHub↗9
  • nus-hpc-ai-lab/edfNUS-HPC-AI-Lab avatar

    NUS-HPC-AI-Lab/EDF

    23View on GitHub↗

    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.

    Python
    View on GitHub↗23

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  • nus-hpc-ai-lab/padNUS-HPC-AI-Lab avatar

    NUS-HPC-AI-Lab/PAD

    21View on GitHub↗

    Matching-based Dataset Distillation methods can be summarized into two steps:

    Python
    View on GitHub↗21
  • saehyung-lee/dccS

    saehyung-lee/dcc

    0View on GitHub↗

    This repository is the official implementation of Dataset Condensation with Contrastive Signals (DCC), published as a conference paper at ICML 2022. The implementation is based on (https://github.com/VICO-UoE/DatasetCondensation).

    View on GitHub↗0
  • shqii1j/seqmatchshqii1j avatar

    shqii1j/seqmatch

    4View on GitHub↗

    Paper

    Python
    View on GitHub↗4
  • sjshin-ai/lcmatSJShin-AI avatar

    SJShin-AI/LCMat

    22View on GitHub↗

    Official PyTorch implementation of "Loss-Curvature Matching for Dataset Selection and Condensation" (AISTATS 2023) by Seungjae Shin, HeeSun Bae, Donghyeok Shin, Weonyoung Joo, and Il-Chul Moon.

    Python
    View on GitHub↗22
  • slyang2021/nsdslyang2021 avatar

    slyang2021/NSD

    0View on GitHub↗

    PyTorch implementation of paper "Neural Spectral Decomposition for Dataset Distillation" in ECCV 2024.

    View on GitHub↗0
  • vico-uoe/datasetcondensationVICO-UoE avatar

    VICO-UoE/DatasetCondensation

    542View on GitHub↗

    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.

    Python
    View on GitHub↗542
  • yongalls/selmatchYongalls avatar

    Yongalls/SelMatch

    9View on GitHub↗
    Python
    View on GitHub↗9
  • zhong0x29a/mctZhong0x29a avatar

    Zhong0x29a/MCT

    5View on GitHub↗

    Wenliang Zhong 1 , Haoyu Tang 1 , Qinghai Zheng 2 , Mingzhu Xu 1 , Yupeng Hu 1 , Weili Guan 3

    Python
    View on GitHub↗5