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[TNNLS 2025] Official PyTorch Implementation of "Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers"

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# Diversify and Conquer (DAC) for Open-Set Semi-Supervised Learning

## Introduction

This is an official repository for our **TNNLS 2025** paper:
> **Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers**</br>
> Heejo Kong, Sung-Jin Kim, Gunho Jung, Seong-Whan Lee*</br>
[[`Paper (IEEE)`](https://doi.org/10.1109/TNNLS.2025.3547801)] [[`Paper (arXiv)`](https://arxiv.org/abs/2505.24443)] [[`Models and Logs`](https://huggingface.co/heejokong/open-set_SSL_divcon/tree/main)] [[`BibTeX`](#citations)]

![](https://raw.githubusercontent.com/heejokong/DivCon/main/assets/framework_1.png)

## Getting Started

This is an example of how to set up DAC locally. We implement DAC using the SSL and Open-set SSL PyTorch benchmarks, [USB](https://github.com/microsoft/Semi-supervised-learning) and [IOMatch](https://github.com/nukezil/IOMatch).

To get a local copy up, running follow these simple example steps.

### Prerequisites

Our open-set SSL benchmark is built on pytorch, with torchvision, torchaudio, and transformers.

To install the required packages, you can create a conda environment:
```sh
conda create --name dac python=3.10
```

then use pip to install required packages:
```sh
pip install -r requirements.txt
```

From now on, you can start use our benchmark by typing
```sh
python train.py --c config/classic_cv/fixmatch/fixmatch_cifar100_50_1250_1.yaml
```

### Dataset Preparation

All datasets are supposed to be under ./data (or create soft links) as follows.
```
DivCon
├── config
    └── ...
├── data
    ├── cifar10
        └── cifar-10-batches-py
    └── cifar100
        └── cifar-100-python
    └── imagenet30
        └── one_class_test
        └── one_class_train
    └── semi-inat-2021
        └── l_train
        └── u_train
        └── test
    └── ood_data
├── semilearn
    └── ...
└── ...
```

The detailed instructions for downloading and processing are shown in Dataset Download. Please follow it to download datasets before running or developing algorithms.

The data For ImageNet-30 and Semi-iNat-2021 can be downloaded from the following repositories, [OpenMatch](https://github.com/VisionLearningGroup/OP_Match) and [semi-inat-2021](https://github.com/cvl-umass/semi-inat-2021).

The out-of-dataset testing data for extended open-set evaluation can be downloaded in [CSI](https://github.com/alinlab/CSI).

### Development

You can also develop your own open-set SSL algorithm and evaluate it by cloning this repository:
```sh
git clone https://github.com/heejokong/DivCon.git
```

## Usage

We implement [Diversify and Conquer (DAC)](./semilearn/algorithms/dac/dac.py) using the codebase of [USB](https://github.com/microsoft/Semi-supervised-learning).

### Training

Here is an example to train DAC on CIFAR-100 with the seen/unseen split of "50/50" and 25 labels per seen class (i.e., the task CIFAR-50-1250 with 1250 labeled samples in total).
```sh
# seed = 1
CUDA_VISIBLE_DEVICES=0 python train.py --c config/openset_cv/dac/dac_cifar100_50_1250_1.yaml
```

Training DAC on other datasets with different open-set SSL settings can be specified by a config file:
```sh
# CIFAR10, seen/unseen split of 6/4, 25 labels per seen class (CIFAR-6-150), seed = 1
CUDA_VISIBLE_DEVICES=0 python train.py --c config/openset_cv/dac/dac_cifar10_6_150_1.yaml

# CIFAR100, seen/unseen split of 20/80, 25 labels per seen class (CIFAR-20-500), seed = 1
CUDA_VISIBLE_DEVICES=0 python train.py --c config/openset_cv/dac/dac_cifar100_20_500_1.yaml

# ImageNet30, seen/unseen split of 20/10, 5% labeled data (ImageNet-20-p5), seed = 1
CUDA_VISIBLE_DEVICES=0 python train.py --c config/openset_cv/dac/dac_in30_p5_1.yaml
```

### Evaluation

After training, the best checkpoints will be saved in ``./saved_models``. The closed-set performance has been reported in the training logs. For the open-set evaluation, please see [``eval.py``](./eval.py).

## Experimental Results

Through hyper-parameter tuning, we achieved slight improvements over the results reported in the TNNLS paper.
For CIFAR-10 and ImageNet-30, experiments were rerun with $\lambda_{mi}$ set to 0.05; for CIFAR-100, with $\lambda_{mi}$ set to 0.10.
The trained models and training logs are available at [this page](https://huggingface.co/heejokong/open-set_SSL_divcon/tree/main).

### Closed-Set Classification Accuracy
|               | CIFAR-6-30 | CIFAR-6-60 | CIFAR-6-150 | ImageNet-20-P1 | ImageNet-20-P5 |
|:-------------:|:----------:|:----------:|:-----------:|:--------------:|:--------------:|
|**Seed=1**     | 87.13      | 93.90      | 92.12       | 86.10          | 93.35          |
|**Seed=2**     | 86.95      | 93.55      | 93.83       | 88.15          | 93.25          |
|**Seed=3**     | 91.38      | 93.70      | 94.05       | 86.40          | 93.50          |
| **Average**   | 88.49      | 93.72      | 93.33       | 86.88          | 93.37          |

|               | CIFAR-20-100 | CIFAR-20-200 | CIFAR-20-500 | CIFAR-50-250 | CIFAR-50-500 | CIFAR-50-1250 |
|:-------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:-------------:|
|**Seed=1**     | 51.45        | 60.30        | 66.95        | 56.56        | 65.40        | 71.26         |
|**Seed=2**     | 59.55        | 62.60        | 68.75        | 56.58        | 64.62        | 70.58         |
|**Seed=3**     | 54.40        | 57.30        | 65.75        | 60.80        | 64.98        | 71.28         |
| **Average**   | 55.13        | 60.07        | 67.15        | 57.98        | 65.00        | 71.04         |

### Open-Set Classification Accuracy
|               | CIFAR-6-30 | CIFAR-6-60 | CIFAR-6-150 | ImageNet-20-P1 | ImageNet-20-P5 |
|:-------------:|:----------:|:----------:|:-----------:|:--------------:|:--------------:|
|**Seed=1**     | 72.18      | 78.07      | 77.35       | 82.09          | 88.69          |
|**Seed=2**     | 72.19      | 77.99      | 78.10       | 77.69          | 89.11          |
|**Seed=3**     | 77.00      | 78.11      | 81.31       | 77.64          | 85.50          |
| **Average**   | 73.79      | 78.06      | 78.92       | 79.14          | 87.77          |

|               | CIFAR-20-100 | CIFAR-20-200 | CIFAR-20-500 | CIFAR-50-250 | CIFAR-50-500 | CIFAR-50-1250 |
|:-------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:-------------:|
|**Seed=1**     | 48.64        | 56.82        | 64.00        | 53.68        | 62.77        | 65.99         |
|**Seed=2**     | 54.03        | 58.57        | 64.74        | 54.59        | 63.08        | 69.03         |
|**Seed=3**     | 51.79        | 54.36        | 62.38        | 58.14        | 62.01        | 67.24         |
| **Average**   | 51.49        | 56.58        | 63.71        | 55.47        | 62.62        | 67.42         |

## Comparisons with Other Baselines

### Closed-Set Classification Accuracy

![](https://raw.githubusercontent.com/heejokong/DivCon/main/assets/result_closed_set.png)

### Open-Set Classification Accuracy

![](https://raw.githubusercontent.com/heejokong/DivCon/main/assets/result_open_set.png)

## Acknowledgments

We sincerely thank the authors of [USB (NeurIPS'22)](https://github.com/microsoft/Semi-supervised-learning) for creating such an awesome SSL benchmark.

We sincerely thank the authors of the following projects for sharing the code of their great works:

- [MTC (ECCV'20)](https://github.com/YU1ut/Multi-Task-Curriculum-Framework-for-Open-Set-SSL)
- [OpenMatch (NeurIPS'21)](https://github.com/VisionLearningGroup/OP_Match)
- [OSP (CVPR'23)](https://github.com/rain305f/OSP)
- [SSB (ICCV'23)](https://github.com/yue-fan/ssb)
- [IOMatch (ICCV'23)](https://github.com/nukezil/IOMatch)
- [UAGreg (AAAI'24)](https://github.com/heejokong/UAGreg)

## Citation
```bibtex
@article{kong2025diversify,
  title={Diversify and Conquer: Open-Set Disagreement for Robust Semi-Supervised Learning With Outliers},
  author={Kong, Heejo and Kim, Sung-Jin and Jung, Gunho and Lee, Seong-Whan},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2025},
  publisher={IEEE}
}
```

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