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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
• progress
• 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 on CIFAR-10

python train.py --gpu 0 --semi_method mix --dataset cifar10 --ratio 2 --num_max 1500 --imb_ratio_l 100 --imb_ratio_u 1 \
--epoch 500 --val-iteration 500

Applying DARP on the baseline algorithm

#python train.py --gpu 0 --darp --est --alpha 2 --warm 200 --semi_method mix --dataset cifar10 --ratio 2 --num_max 1500 --imb_ratio_l 100 --imb_ratio_u 1  \
--epoch 500 --val-iteration 500