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DARP: Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning

This repository contains code for the paper "Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning" by Jaehyung Kim, Youngbum Hur, Sejun Park, Eunho Yang, Sung Ju Hwang, and Jinwoo Shin.

Dependencies

  • python3
  • pytorch == 1.1.0
  • torchvision
  • scipy
  • randAugment (Pytorch re-implementation: https://github.com/ildoonet/pytorch-randaugment)

Scripts

Please check out run.sh for the scripts to run the baseline algorithms and ours (DARP).

Training procedure of DARP

Train a network with baseline algorithm, e.g., MixMatch

python train_mix.py --gpu 0 --ratio 2 --num_max 1500 --imb_ratio_l 100 --imb_ratio_u 1 \
--epoch 500 --val-iteration 500 --out cifar10@N_1500_r_100_1_mix

Applying DARP on the baseline algorithm

#python train_mix.py --darp --est --alpha 2 --warm 200 --gpu 0 --ratio 2 --num_max 1500 --imb_ratio_l 100 --imb_ratio_u 1  \
--epoch 500 --val-iteration 500 --out cifar10@N_1500_r_100_1_mix_darp 

darp's People

Contributors

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