Boosting the Certified Robustness of L-infinity Distance Nets
Authors: Bohang Zhang, Du Jiang, Di He, Liwei Wang
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that using the proposed training strategy, the certified accuracy of ℓ -distance net can be dramatically improved from 33.30% to 40.06% on CIFAR-10 (ϵ = 8/255), meanwhile outperforming other approaches in this area by a large margin. |
| Researcher Affiliation | Collaboration | 1Key Laboratory of Machine Perception, MOE, School of Artificial Intelligence, Peking University 2Microsoft Research 3International Center for Machine Learning Research, Peking University |
| Pseudocode | No | The paper describes its methods using mathematical equations and prose but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Codes are available at https://github.com/zbh2047/L inf-dist-net-v2. |
| Open Datasets | Yes | We evaluate the proposed training strategy on benchmark datasets MNIST and CIFAR-10 to show the effectiveness of ℓ -distance net. |
| Dataset Splits | No | The paper mentions 'training dataset' and 'test dataset' (e.g., Figure 1(b)), and uses standard benchmark datasets (MNIST, CIFAR-10), which often come with predefined splits. However, it does not explicitly state the train/validation/test split percentages, sample counts, or the methodology used to create these splits. |
| Hardware Specification | Yes | All experiments are run for 8 times on a single NVIDIA Tesla-V100 GPU |
| Software Dependencies | Yes | Our experiments are implemented using the Pytorch framework. ... The CUDA version is 11.2. |
| Experiment Setup | Yes | In all experiments, we choose the Adam optimizer with a batch size of 512. The learning rate is set to 0.03 initially and decayed using a simple cosine annealing throughout the whole training process. ... The ℓp-relaxation starts at p = 8 and ends at p = 1000 with p increasing exponentially. Accordingly, the mixing coefficient λ decays exponentially during the ℓp-relaxation process from λ0 to a vanishing value λend. ... All hyper-parameters are provided in Table 3. |