Adversarial Distributional Training for Robust Deep Learning
Authors: Yinpeng Dong, Zhijie Deng, Tianyu Pang, Jun Zhu, Hang Su
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on several benchmarks validate the effectiveness of ADT compared with the state-of-the-art AT methods. |
| Researcher Affiliation | Collaboration | Yinpeng Dong , Zhijie Deng , Tianyu Pang, Jun Zhu, Hang Su Dept. of Comp. Sci. & Tech., Institute for AI, BNRist Center Tsinghua-Bosch Joint ML Center, THBI Lab, Tsinghua University, Beijing, 100084 China |
| Pseudocode | Yes | We provide the general algorithm for ADT in Alg. 1. Algorithm 1 The general algorithm for ADT |
| Open Source Code | Yes | Code is available at https://github.com/dongyp13/Adversarial-Distributional-Training. |
| Open Datasets | Yes | We perform experiments on the CIFAR10 [34], CIFAR-100 [34], and SVHN [44] datasets. |
| Dataset Splits | No | The paper mentions using test sets for evaluation but does not explicitly detail the train/validation/test dataset splits, such as percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running its experiments. |
| Software Dependencies | No | The paper mentions using Adam for optimization but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Training Details: We adopt the cross-entropy loss as L in our objective (5). We set λ = 0.01 for the entropy term, and leave the study of the effects of λ in Sec. 5.3. For ADTEXP, we adopt Adam [32] for optimizing φi with the learning rate 0.3, the optimization steps T = 7, and the number of MC samples in each step k = 5. |