Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Towards Understanding Dual BN In Hybrid Adversarial Training
Authors: Chenshuang Zhang, Chaoning Zhang, Kang Zhang, Axi Niu, Junmo Kim, In So Kweon
TMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this work, we perform experiments on CIFAR10 (Krizhevsky et al., 2009; Andriushchenko & Flammarion, 2020; Zhang et al., 2022) with Res Net18 (Andriushchenko & Flammarion, 2020; Targ et al., 2016; Wu et al., 2019; Li et al., 2016; Zhang et al., 2022). Specifically, we train the model for 110 epochs. The learning rate is set to 0.1 and decays by a factor of 0.1 at the epoch 100 and 105. We adopt an SGD optimizer with weight decay 5 × 10−4. For generating adversarial examples during training, we use ℓ PGD attack with 10 iterations and step size α = 2/255. For the perturbation constraint, ϵ is set to ℓ 8/255 (Pang et al., 2020) or 16/255 (Xie & Yuille, 2020). Following Pang et al. (2020), we evaluate the model robustness under PGD-10 attack (PGD attack with 10 steps) and Auto Attack (AA) (Croce & Hein, 2020). |
| Researcher Affiliation | Academia | Chenshuang Zhang EMAIL KAIST Chaoning Zhang EMAIL Kyung Hee University Kang Zhang EMAIL KAIST Axi Niu EMAIL Northwestern Polytechnical University Junmo Kim EMAIL KAIST In So Kweon EMAIL KAIST |
| Pseudocode | No | The paper describes methods and procedures in narrative text and does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing code, a link to a code repository, or a mention of code being available in supplementary materials. |
| Open Datasets | Yes | In this work, we perform experiments on CIFAR10 (Krizhevsky et al., 2009; Andriushchenko & Flammarion, 2020; Zhang et al., 2022) with Res Net18 |
| Dataset Splits | Yes | In this work, we perform experiments on CIFAR10 (Krizhevsky et al., 2009; Andriushchenko & Flammarion, 2020; Zhang et al., 2022) with Res Net18 (Andriushchenko & Flammarion, 2020; Targ et al., 2016; Wu et al., 2019; Li et al., 2016; Zhang et al., 2022). Specifically, we train the model for 110 epochs. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU model, CPU type) used for running the experiments. |
| Software Dependencies | No | The paper mentions general tools and techniques like 'SGD optimizer' but does not specify any software libraries or frameworks with version numbers (e.g., Python version, PyTorch version). |
| Experiment Setup | Yes | Specifically, we train the model for 110 epochs. The learning rate is set to 0.1 and decays by a factor of 0.1 at the epoch 100 and 105. We adopt an SGD optimizer with weight decay 5 × 10−4. For generating adversarial examples during training, we use ℓ PGD attack with 10 iterations and step size α = 2/255. For the perturbation constraint, ϵ is set to ℓ 8/255 (Pang et al., 2020) or 16/255 (Xie & Yuille, 2020). Following Pang et al. (2020), we evaluate the model robustness under PGD-10 attack (PGD attack with 10 steps) and Auto Attack (AA) (Croce & Hein, 2020). |