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Improving Bayesian Neural Networks by Adversarial Sampling

Conference

Model training

python3 main.py

Note that the default path to datasets is ~/data. You can modify it in line 31, main.py.

Environment

  • python: 3.7.9

  • torch: 1.7.1

  • torchvision: 0.8.2

  • numpy: 1.19.2

Core codes

The Adversarial Sampling and calculation of the Adversarial loss is implemented in line 119 - 152, utils.py:

# Initialize epsilon with random unit Gaussian variable
with torch.no_grad():
    for module in model.modules():
        if type(module).__name__ in ["RandConv2d", "RandLinear", "RandBatchNorm2d"]:
            module.eps_weight.normal_()
            module.eps_weight.requires_grad = True
            if module.eps_bias is not None:
                module.eps_bias.normal_()
                module.eps_bias.requires_grad = True

alpha = 0.02
iters = 5


for index in range(iters):
    for module in model.modules():
        if type(module).__name__ in ["RandConv2d", "RandLinear", "RandBatchNorm2d"]:
            module.eps_weight.requires_grad = True
            if module.eps_bias is not None:
                module.eps_bias.requires_grad = True

    routput, rkl = model(input_var, fix=True)
    model.zero_grad()
    rloss = - F.cross_entropy(routput, target) # Adversarial loss
    rloss.backward()
    # Updating
    with torch.no_grad():
        for module in model.modules():
            if type(module).__name__ in ["RandConv2d", "RandLinear", "RandBatchNorm2d"]:
                module.eps_weight -= alpha * module.eps_weight.grad.sign()
                if module.eps_bias is not None:
                    module.eps_bias -= alpha * module.eps_bias.grad.sign()
routput, rkl = model(input_var, fix=True)
rloss = F.cross_entropy(routput, target) # Adversarial loss

Cite

@article{Zhang_Hua_Song_Wang_Xue_Ma_Guan_2022, 
    title={Improving Bayesian Neural Networks by Adversarial Sampling}, 
    volume={36}, 
    url={https://ojs.aaai.org/index.php/AAAI/article/view/21250}, 
    DOI={10.1609/aaai.v36i9.21250}, 
    number={9}, 
    journal={Proceedings of the AAAI Conference on Artificial Intelligence}, 
    author={Zhang, Jiaru and Hua, Yang and Song, Tao and Wang, Hao and Xue, Zhengui and Ma, Ruhui and Guan, Haibing}, 
    year={2022}, 
    month={Jun.}, 
    pages={10110-10117} 
}

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