Visualizing the Emergence of Intermediate Visual Patterns in DNNs
Authors: Mingjie Li, Shaobo Wang, Quanshi Zhang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we used our method to visualize sample features and regional features in VGG-16 [49], Res Net-34/50 [21], Mobile Net-V2 [44], which were learned for object classification, based on the Tiny Image Net dataset [26], the MS COCO 2014 dataset [30], and the CUB-200-2011 dataset [55]. |
| Researcher Affiliation | Academia | Mingjie Li Shanghai Jiao Tong University limingjie0608@sjtu.edu.cn Shaobo Wang Harbin Institute of Technology 181110315@stu.hit.edu.cn Quanshi Zhang Shanghai Jiao Tong University zqs1022@sjtu.edu.cn |
| Pseudocode | No | The paper describes its algorithms in prose and mathematical formulations but does not contain any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about open-source code availability for the methodology described, nor does it include links to a code repository. |
| Open Datasets | Yes | In this section, we used our method to visualize sample features and regional features in VGG-16 [49], Res Net-34/50 [21], Mobile Net-V2 [44], which were learned for object classification, based on the Tiny Image Net dataset [26], the MS COCO 2014 dataset [30], and the CUB-200-2011 dataset [55]. |
| Dataset Splits | No | The paper mentions using images for both training and testing for certain datasets but does not provide specific details on dataset splits (e.g., percentages, sample counts, or explicit validation sets). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, required to replicate the experiment. |
| Experiment Setup | Yes | We set α = 0.1. The attack was iterated for 20 steps with the step size of 0.1 255. We selected the feature before the last fully-connected layer as the sample feature f. We estimated regional importance w(r) with κ set to 1000. We could simply set τ = 0.4. |