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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Adversarial Distributional Training for Robust Deep Learning
Authors: Yinpeng Dong, Zhijie Deng, Tianyu Pang, Jun Zhu, Hang Su
NeurIPS 2020 | Venue PDF | 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. |