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..
Robust Perception through Equivariance
Authors: Chengzhi Mao, Lingyu Zhang, Abhishek Vaibhav Joshi, Junfeng Yang, Hao Wang, Carl Vondrick
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical experiments show that restoring feature equivariance at inference time defends against worst-case adversarial perturbations. The method obtains improved adversarial robustness on four datasets (Image Net, Cityscapes, PASCAL VOC, and MS-COCO) on image recognition, semantic segmentation, and instance segmentation tasks. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Columbia University, New York, USA 2Department of Computer Science, Rutgers University, New Jersey, USA. |
| Pseudocode | Yes | Algorithm 1 Equivariance Defense |
| Open Source Code | Yes | Our code is available at https: //github.com/cvlab-columbia/Equi4Rob. |
| Open Datasets | Yes | Our experiments evaluate the adversarial robustness on four datasets: Image Net (Deng et al., 2009), Cityscapes (Cordts et al., 2016), PASCAL-VOC (Everingham et al., 2010), and MS-COCO (Lin et al., 2014). |
| Dataset Splits | No | The paper mentions 'randomly sample 2% of data for evaluation' for ImageNet, but does not provide specific train/validation/test dataset splits (percentages or counts) for any of the datasets. |
| Hardware Specification | Yes | We evaluate on a single A6000 GPU. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We choose the number of transformations to be K = 8, which empirically can be fit into a 2080Ti GPU with batch size 1. ... We use steps T = 20 for all our defense tasks. |