Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Scalable Certified Segmentation via Randomized Smoothing
Authors: Marc Fischer, Maximilian Baader, Martin Vechev
ICML 2021 | Venue PDF | LLM Run Details | Input Tokens: 22,108 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,722 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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). |