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 [1].
Localized Randomized Smoothing for Collective Robustness Certification
Authors: Jan Schuchardt, Tom Wollschläger, Aleksandar Bojchevski, Stephan Günnemann
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we compare our method to all existing collective certificates for ℓp-norm perturbations: Center smoothing using isotropic Gaussian noise (Kumar & Goldstein, 2021), Seg Certify (Fischer et al., 2021) and the collective certificates of Schuchardt et al. (2021). |
| Researcher Affiliation | Academia | 1Technical University of Munich 2CISPA Helmholtz Center for Information Security |
| Pseudocode | No | The paper describes the proposed methods and derivations mathematically but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | An implementation will be made available at https://www.cs.cit.tum.de/daml/localized-smoothing. |
| Open Datasets | Yes | We evaluate our certificate for l2 perturbations on 100 images from the Pascal VOC (Everingham et al., 2010) 2012 segmentation validation set. Training is performed on 10582 samples extracted from SBD, also known as Pascal trainaug (Hariharan et al., 2011). |
| Dataset Splits | Yes | We evaluate our certificate for l2 perturbations on 100 images from the Pascal VOC (Everingham et al., 2010) 2012 segmentation validation set. |
| Hardware Specification | Yes | The experiments on Pascal-VOC with strictly local models (Fig. 2) were performed using a Xeon E5-2630 v4 CPU @ 2.20GHz, an NVIDA GTX 1080TI GPU and 128 GB of RAM. All other experiments were performed using an AMD EPYC 7543 CPU @ 2.80GHz, an NVIDA A100 GPU and 128 GB of RAM. |
| Software Dependencies | Yes | The collective linear program is solved using MOSEK (version 9.2.46) (MOSEK Ap S, 2019) through the CVXPY interface (version 1.1.13) (Diamond & Boyd, 2016). |
| Experiment Setup | Yes | We train our models for 512 epochs, using Dice loss and Adam(lr = 0.001, β1 = 0.9, β2 = 0.999, ϵ = 10-8, weight decay = 0). We use a batch size of 128 for Pascal-VOC and a batch size of 32 for Cityscapes. |