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..
Tightening Robustness Verification of Convolutional Neural Networks with Fine-Grained Linear Approximation
Authors: Yiting Wu, Min Zhang11674-11681
AAAI 2021 | Venue PDF | 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. |