Scalable Certified Segmentation via Randomized Smoothing
Authors: Marc Fischer, Maximilian Baader, Martin Vechev
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental evaluation on synthetic data and challenging datasets, such as Pascal Context, Cityscapes, and Shape Net, shows that our algorithm can achieve, for the first time, competitive accuracy and certification guarantees on real-world segmentation tasks. |
| Researcher Affiliation | Academia | 1Department of Computer Science, ETH Zurich, Switzerland. Correspondence to: Marc Fischer <marc.fischer@inf.ethz.ch>. |
| Pseudocode | Yes | Algorithm 1 adapted from (Cohen et al., 2019) |
| Open Source Code | Yes | We provide an implementation at https://github.com/ eth-sri/segmentation-smoothing. |
| Open Datasets | Yes | Our experimental evaluation on synthetic data and challenging datasets, such as Pascal Context, Cityscapes, and Shape Net, shows that our algorithm can achieve, for the first time, competitive accuracy and certification guarantees on real-world segmentation tasks. |
| Dataset Splits | No | The paper mentions using datasets like Cityscapes, Pascal Context, and ShapeNet, and refers to 'standard split of the dataset where available' in Appendix A.1, but it does not provide specific percentages, sample counts, or explicit citations to predefined splits in the main text or the relevant appendix section needed to reproduce the data partitioning. |
| Hardware Specification | Yes | All timings are for a single Nvidia Ge Force RTX 2080 Ti. |
| Software Dependencies | Yes | We used Python 3.8 and PyTorch 1.9 with CUDA 11.1 for all experiments. |
| Experiment Setup | Yes | All SEGCERTIFY (n0 = 10, α = 0.001) results are certifiably robust at radius R w.h.p. SEGCERTIFY n = 100, τ = 0.75 We trained our base models with Gaussian Noise (σ = 0.25), as in Cohen et al. (2019). |