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..
Visualizing the Emergence of Intermediate Visual Patterns in DNNs
Authors: Mingjie Li, Shaobo Wang, Quanshi Zhang
NeurIPS 2021 | Venue PDF | 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 EMAIL Shaobo Wang Harbin Institute of Technology EMAIL Quanshi Zhang Shanghai Jiao Tong University EMAIL |
| 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. |