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This is the repository for holding the sample code of Fast Abductive Learning by Similarity-based Consistency Optimization in NeurIPS 2021.
This code is only tested in Linux environment.
- Ubuntu 18.04
- Python 3.7
- PyTorch 1.7
- CuPy 8.3
- tqdm
- scikit-learn
- opencv-python
To create the above environment with Anaconda, you can run the following command (cudatoolkit=10.1 for old GPUs, cudatoolkit=11.3 for new GPUs / new drivers):
(cudatoolkit=10.1)
conda create -n ablsim python=3.7 -y
conda activate ablsim
conda install -c conda-forge cupy cudatoolkit=10.1 -y
conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=10.1 -c pytorch -y
pip install tqdm opencv-python scikit-learn matplotlib
(cudatoolkit=11.3)
conda create -n ablsim python=3.7 -y
conda activate ablsim
conda install -c conda-forge cupy cudatoolkit=11.3 -y
conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch -y
pip install tqdm opencv-python scikit-learn matplotlib
To reproduce the experiment results, simply run the following code:
Download the Handwritten_Math_Symbols.zip from google drive and unzip it:
unzip Handwritten_Math_Symbols.zip -d data
-
MNIST (CIFAR-10) Addition
python main_1_2.py --dataset 2ADD --images handwritten python main_1_2.py --dataset 2ADD --images CIFAR
-
Handwritten Formula Recognition
python main_1_2.py --dataset HWF --images handwritten python main_1_2.py --dataset HWF --images CIFAR
-
CIFAR-10 Decimal Equation Decipherment
Download the images.zip and ssl_mode.zip from google drive and unzip it:
unzip images.zip -d data unzip ssl_model.zip python main_3.py --images CIFAR
To view or change the hyperparameters, please refer to the arg_init() function in the code.
@incollection{ablsim2021huang,
author = {Huang, Yu-Xuan and Dai, Wang-Zhou and Cai, Le-Wen and Muggleton, Stephen H and Jiang, Yuan},
booktitle = {Advances in Neural Information Processing Systems 34},
pages = {26574--26584},
title = {Fast Abductive Learning by Similarity-based Consistency Optimization},
year = {2021}
}