Tightening Robustness Verification of Convolutional Neural Networks with Fine-Grained Linear Approximation
Authors: Yiting Wu, Min Zhang11674-11681
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate it with open-source benchmarks, including Le Net and the models trained on MNIST and CIFAR. Experimental results show that Deep Cert outperforms other state-of-the-art robustness verification tools with at most 286.3% improvement to the certified lower bound and 1566.8 times speedup for the same neural networks. |
| Researcher Affiliation | Academia | Yiting Wu,1 Min Zhang1,2 1 Shanghai Key Laboratory for Trustworthy Computing, East China Normal University 2 Shanghai Institute of Intelligent Science and Technology, Tongji University |
| Pseudocode | Yes | Algorithm 1: Binary search for lower robustness bound |
| Open Source Code | No | The paper states 'We implement Deep Cert, the resulting verification toolkit.' and 'We implement our approach atop CNN-Cert in Python as an extension named Deep Cert.', but it does not provide any explicit statement or link regarding the open-sourcing of Deep Cert's code. |
| Open Datasets | Yes | We evaluate it with open-source benchmarks, including Le Net and the models trained on MNIST and CIFAR. Experimental results show that Deep Cert outperforms other state-of-the-art robustness verification tools with at most 286.3% improvement to the certified lower bound and 1566.8 times speedup for the same neural networks. |
| Dataset Splits | No | The paper mentions training on datasets and using 'test images' but does not specify the splits (e.g., percentages or counts) for training, validation, or testing subsets. |
| Hardware Specification | Yes | All the experiments were conducted on a workstation running an 8core Intel Xeon CPU E5-2620 v4, 32 GB of RAM, and an NVIDIA Tesla K80 GPU. |
| Software Dependencies | No | The paper states 'We implement our approach atop CNN-Cert in Python as an extension named Deep Cert.' While Python is mentioned, no specific version number for Python or any other libraries/dependencies (e.g., PyTorch, TensorFlow, etc.) is provided. |
| Experiment Setup | No | The paper describes network architectures but does not provide specific experimental setup details such as hyperparameters (e.g., learning rate, batch size, number of epochs) or optimizer settings. |