Code for the paper: "ActiveMatch: End-to-end Semi-supervised Active Representation Learning" by Xinkai Yuan, Zilinghan Li, and Gaoang Wang.
conda env create --name activematch --file environment.yml
conda activate activematch
python train.py
Train our model with 40 to 200 labels of CIFAR-10:
python train.py --dataset cifar10 --num-labeled 40 --batch-size 64 --lr 0.03 --expand-labels --seed 5 --out results/cifar10@40-200 --stop-active 200 --num-sample 32 --epoch-warmup 15
Train our model with 200 to 1000 labels of CIFAR-100:
python train.py --dataset cifar100 --num-labeled 200 --batch-size 64 --lr 0.03 --expand-labels --seed 5 --out results/cifar10@200-1000 --stop-active 1000 --num-sample 4 --epoch-warmup 15
@INPROCEEDINGS{9898008,
author={Yuan, Xinkai and Li, Zilinghan and Wang, Gaoang},
booktitle={2022 IEEE International Conference on Image Processing (ICIP)},
title={ActiveMatch: End-To-End Semi-Supervised Active Representation Learning},
year={2022},
volume={},
number={},
pages={1136-1140},
doi={10.1109/ICIP46576.2022.9898008}
}