https://github.com/3neutronstar/GuidedMixup

Official PyTorch implementation of "GuidedMixup An Efficient Mixup Strategy Guided by Saliency Maps" (AAAI'23 Oral)
https://github.com/3neutronstar/GuidedMixup

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Official PyTorch implementation of "GuidedMixup An Efficient Mixup Strategy Guided by Saliency Maps" (AAAI'23 Oral)

• Host: GitHub
• URL: https://github.com/3neutronstar/GuidedMixup
• Owner: 3neutronstar
• License: gpl-3.0
• Created: 2023-09-25T13:37:48.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-07-09T14:31:16.000Z (4 months ago)
• Last Synced: 2024-07-09T18:32:43.619Z (4 months ago)
• Language: Python
• Size: 1.66 MB
• Stars: 3
• Watchers: 1
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# GuidedMixup

Official PyTorch implementation of "GuidedMixup: An Efficient Mixup Strategy Guided by Saliency Maps" (AAAI'23, Oral) ([paper](https://arxiv.org/abs/2306.16612)) 

![Image](./assets/method.png)

## Requirements

To install requirements:

```setup

pip install -r requirements.txt

```

Install pairing Algorithm:

```setup

python setup.py build_ext --inplace

```

## Training

-----

We provide the code for training the neural network above general classification datasets from [PuzzleMix](https://github.com/snu-mllab/PuzzleMix).

### Cifar-100

- To reproduce **Guided-SR with PreActResNet18 for 1200 epochs**, run:

```

python main.py --dataset cifar100 --data_dir [data_path] --root_dir [save_path] --labels_per_class 500 --arch preactresnet18  --learning_rate 0.1 --momentum 0.9 --decay 0.0001 --epochs 1200 --schedule 400 800 --gammas 0.1 0.1 --train mixup --guided True --condition greedy --mix_prob 0.5 --guided_type sr

```

- To reproduce **Guided-AP with PreActResNet18 for 1200 epochs**, run:

```

python main.py --dataset cifar100 --data_dir [data_path] --root_dir [save_path] --labels_per_class 500 --arch preactresnet18  --learning_rate 0.1 --momentum 0.9 --decay 0.0001 --epochs 1200 --schedule 400 800 --gammas 0.1 0.1 --train mixup --guided True --condition greedy --mix_prob 0.8 --guided_type ap

```

### Tiny-ImageNet

- To reproduce **Guided-SR with PreActResNet18 for 1200 epochs**, run:

```

python main.py --dataset tiny-imagenet-200 --data_dir [data_path] --root_dir [save_path] --labels_per_class 500 --arch preactresnet18  --learning_rate 0.2 --momentum 0.9 --decay 0.0001 --epochs 1200 --schedule 600 900 --gammas 0.1 0.1 --train mixup --guided True --condition greedy --mix_prob 0.5 --guided_type sr

```

- To reproduce **Guided-AP with PreActResNet18 for 1200 epochs**, run:

```

python main.py --dataset tiny-imagenet-200 --data_dir [data_path] --root_dir [save_path] --labels_per_class 500 --arch preactresnet18  --learning_rate 0.2 --momentum 0.9 --decay 0.0001 --epochs 1200 --schedule 600 900 --gammas 0.1 0.1 --train mixup --guided True --condition greedy --mix_prob 0.8 --guided_type ap --clean_lam 1.0

```

## Citing this Work and this Implementation

```

@inproceedings{kang2023guidedmixup,

  title={GuidedMixup: an efficient mixup strategy guided by saliency maps},

  author={Kang, Minsoo and Kim, Suhyun},

  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},

  volume={37},

  number={1},

  pages={1096--1104},

  year={2023}

}

```