BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining
Authors: Weizhe Hua, Yichi Zhang, Chuan Guo, Zhiru Zhang, G. Edward Suh
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our technique to several existing robust training algorithms and achieve a 2.2 speed-up for TRADES and MART on CIFAR-10 and a 1.7 speed-up for Aug Mix on CIFAR-10-C and CIFAR-100-C without any reduction in clean and robust accuracy. 4 Experiments |
| Researcher Affiliation | Collaboration | Weizhe Hua1, Yichi Zhang1, Chuan Guo2, Zhiru Zhang1, G. Edward Suh1,2 1Cornell University, 2Facebook AI Research |
| Pseudocode | Yes | Algorithm 1: Standard robust DNN training algorithm. Algorithm 2: Bullet Train. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We apply our technique to several existing robust training algorithms and achieve a 2.2 speed-up for TRADES and MART on CIFAR-10 and a 1.7 speed-up for Aug Mix on CIFAR-10-C and CIFAR-100-C without any reduction in clean and robust accuracy. MNIST (Le Cun et al., 2010) CIFAR-10 (Krizhevsky, 2009). |
| Dataset Splits | No | The paper mentions 'training set' and 'corrupted test samples' for evaluation, implying train/test splits, but it does not specify exact percentages, sample counts for training, validation, and test splits, nor does it explicitly mention a validation set. |
| Hardware Specification | No | The paper states 'using a single NVIDIA GPU' but does not provide specific model numbers or detailed specifications of the hardware used for experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | When Bullet Train is applied, we set NB = 10 with α = 0.007, NR ∈ [0, 2] with α = 1.7ϵ/NR, NO = 0, and γ = 0.8. Figure 6 shows that the accuracy and theoretical speedup of Bullet Train under different γ ∈ [0.65, 0.9]. |