NorMatch

Official PyTorch implementation of NorMatch: Matching Normalizing Flows with Discriminative Classifiers for Semi-Supervised Learning which has been accepted to Transactions on Machine Learning Research (TMLR).

The code is based on 1) Semi-Supervised Learning with Normalizing Flows of which the implementation is here; 2) FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence and its pytorch implementation.

Training

Requirements

python 3.6+ (python 3.10.13 is actually used)
torch 1.12.1
torchvision 0.13.1
cudatoolkit 11.3.1
tensorboard
numpy
tqdm
scipy
torchcontrib
apex (optional)

To install the above packages, please run the following instructions.

conda create -n normatch python=3.10.13
conda activate normatch
conda install pytorch==1.12.1 torchvision==0.13.1 cudatoolkit=11.3 -c pytorch
conda install tensorboard tqdm scipy
pip install torchcontrib

Data preparation

Create a folder named data. STL10 and CIFAR-10/100 can be automatically downloaded when training model. Mini-ImageNet should be prepared according to Label Propagation for Deep Semi-Supervised Learning. The folder structure looks like

|-- data
    |-- stl10
    |-- imagenet
        |-- mini_imagenet
    |-- cifar-100-python
    |-- cifar-10-batches-py

Training models

The training script is train_normatch.sh. A simple example in the script to train the model by 40 labeled data of CIFAR-10 dataset is:

python train_normatch.py --dataset cifar10 --num-labeled 40 --arch wideresnet --batch-size 64 --lr 0.03 --expand-labels --seed 2 \
 --out ./result_ssl_cifar/cifar10_nflow@40_da_ema_onehot  --no-progress --lambda-flow-unsup 0.000001 \
 --flow-dist-trainable --use-ema --dist_align  --no_onehot

The path specified by --out will be created to save the checkpoints. --no_onehot applies to the datasets excluding the STL10 (see Implementation Details of Section 4.1 in our paper: "on STL-10 where a one-hot version is used"). --dist_align is not used in the ablation study of our paper.

To resume from a checkpoint, please add the --resume <path_to_ckpt> option.

To use one more FlowGMM, please use the train_normatch_multi_head.py file together with --use_two_flows option.

To train a FixMatch baseline, please use train_fixmatch.sh.

Citations

@article{deng2022normatch,
  title={NorMatch: Matching Normalizing Flows with Discriminative Classifiers for Semi-Supervised Learning},
  author={Deng, Zhongying and Ke, Rihuan and Schonlieb, Carola-Bibiane and Aviles-Rivero, Angelica I},
  journal={arXiv preprint arXiv:2211.09593},
  year={2022}
}

Zhongying-Deng / NorMatch