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..
Weakly-supervised Discovery of Visual Pattern Configurations
Authors: Hyun Oh Song, Yong Jae Lee, Stefanie Jegelka, Trevor Darrell
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments on the challenging PASCAL VOC dataset, we find the inclusion of our discriminative, automatically detected configurations to outperform all existing state-of-the-art methods. |
| Researcher Affiliation | Academia | University of California, Berkeley *University of California, Davis |
| Pseudocode | No | The paper describes algorithms in paragraph form, such as the greedy algorithm, but does not provide structured pseudocode or algorithm blocks with formal labeling. |
| Open Source Code | No | The paper does not include any explicit statements about releasing source code or provide links to a code repository for the described methodology. |
| Open Datasets | Yes | In our experiments on the challenging PASCAL VOC dataset, we find the inclusion of our discriminative, automatically detected configurations to outperform all existing state-of-the-art methods. |
| Dataset Splits | No | The paper mentions using the PASCAL test set but does not specify a separate validation set or describe how data was split for validation purposes. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as CPU or GPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper mentions using 'fc7 features from the CNN model [6]' and a 'region based detection framework [13, 29]', but it does not specify version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | For discriminative patch discovery, we use K = |P|/2, θ = K/20. For correspondence detection, we discretize the 4D transform space of {x: relative horizontal shift, y: relative vertical shift, s: relative scale, p: relative aspect ratio} with x = 30 px, y = 30 px, s = 1 px/px, p = 1 px/px. |