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..
ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints
Authors: Yinpeng Dong, Shouwei Ruan, Hang Su, Caixin Kang, Xingxing Wei, Jun Zhu
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to evaluate the viewpoint robustness of image classifiers on the Image Net [45] dataset. Our results demonstrate that View Fool can effectively generate a distribution of adversarial viewpoints against the common image classifiers, which also exhibit high transferability across different models. |
| Researcher Affiliation | Collaboration | 1 Dept. of Comp. Sci. and Tech., Institute for AI, Tsinghua-Bosch Joint ML Center, THBI Lab, BNRist Center, Tsinghua University, Beijing 100084, China 2 Institute of Artificial Intelligence, Beihang University, Beijing 100191, China 3 Real AI 4 Peng Cheng Laboratory 5 Pazhou Laboratory (Huangpu), Guangzhou, China |
| Pseudocode | No | The paper describes its optimization algorithm in Section 3.3 using mathematical equations and textual explanations, but it does not present it as a structured pseudocode or algorithm block. |
| Open Source Code | Yes | The code to reproduce the experimental results is publicly available at https://github.com/Heathcliff-saku/View Fool_. |
| Open Datasets | Yes | We consider visual recognition models on Image Net [45] in the experiments. ... Moreover, we introduce a new OOD dataset called Image Net-V to benchmark viewpoint robustness. |
| Dataset Splits | No | The paper mentions evaluating models on ImageNet and ImageNet-V, but it does not explicitly specify the proportions or sizes of training, validation, or test splits used for its experiments. |
| Hardware Specification | No | The provided paper text does not contain specific details about the hardware used for experiments, such as exact GPU/CPU models or memory specifications. While the ethics checklist points to Appendix C.1 for this information, Appendix C.1 itself is not included in the provided text. |
| Software Dependencies | No | The paper mentions tools like "COLMAP [48]" and the "Adam optimizer [28]" but does not specify version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | In View Fool, we initialize the camera at [0, 4, 0] as shown in Fig. 2. We set the range of rotation angles as 2 [ 180 , 180 ], 2 [ 30 , 30 ], φ 2 [20 , 160 ], and the range of translation distances as x 2 [ 0.5, 0.5], y 2 [ 1, 1], z 2 [ 0.5, 0.5]. ... We set λ = 0.01 in the experiments ... We approximate the gradients in Eq. (6) with k = 50 MC samples and adopt the Adam optimizer [28] to update the distribution parameters (µ, σ) for 100 iterations. |