Accelerated Stochastic Gradient-free and Projection-free Methods
Authors: Feihu Huang, Lue Tao, Songcan Chen
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The extensive experimental results on black-box adversarial attack and robust black-box classification demonstrate the efficiency of our algorithms. |
| Researcher Affiliation | Academia | 1College of Computer Science & Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China 2MIIT Key Laboratory of Pattern Analysis & Machine Intelligence. |
| Pseudocode | Yes | Algorithm 1 Acc-SZOFW Algorithm; Algorithm 2 Acc-SZOFW* Algorithm |
| Open Source Code | Yes | Our implementation is based on Py Torch and the code to reproduce our results is publicly available at https://github.com/TLMichael/Acc-SZOFW. |
| Open Datasets | Yes | In the experiment, we use the pre-trained DNN models on MNIST (Le Cun et al., 2010) and CIFAR10 (Krizhevsky et al., 2009) datasets as the target black-box models... In the experiment, we use four public real datasets1. These data are from the website https://www.csie. ntu.edu.tw/ cjlin/libsvmtools/datasets/ |
| Dataset Splits | No | The paper specifies training and testing splits for one task ("half of the samples as training data and the rest as testing data"), but does not explicitly mention a separate validation set or describe how it was used for model selection or hyperparameter tuning. It refers to "pre-trained DNN models" for another task, implying no training/validation was done by the authors on those specific models. |
| Hardware Specification | Yes | All of our experiments are conducted on a server with an Intel Xeon 2.60GHz CPU and an NVIDIA Titan Xp GPU. |
| Software Dependencies | No | Our implementation is based on Py Torch and the code to reproduce our results is publicly available at https://github.com/TLMichael/Acc-SZOFW. While PyTorch is mentioned, no specific version number is provided for reproducibility. |
| Experiment Setup | Yes | In the SAP experiment, we choose ε = 0.3 for MNIST and ε = 0.1 for CIFAR10. In the UAP experiment, we choose ε = 0.3 for both MNIST dataset and CIFAR10 dataset. For fair comparison, we choose the mini-batch size b = 20 for all stochastic zeroth-order methods. We set σ = 10 and θ = 10. For fair comparison, we choose the mini-batch size b = 100 for all stochastic zeroth-order methods. |