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..
Predicting the Politics of an Image Using Webly Supervised Data
Authors: Christopher Thomas, Adriana Kovashka
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally show that our method outperforms numerous baselines on both a large held-out webly supervised test set, and the set of crowdsourced annotations. |
| Researcher Affiliation | Academia | Christopher Thomas Adriana Kovashka Department of Computer Science University of Pittsburgh Pittsburgh, PA 15213 EMAIL |
| Pseudocode | No | The paper describes its method verbally and with a diagram, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our dataset, code, and additional materials are available online for download here: http://www.cs.pitt.edu/ chris/politics |
| Open Datasets | Yes | We propose and make available1 a very large dataset of biased images with paired text, and a large amount of diverse crowdsourced annotations regarding political bias. Our dataset, code, and additional materials are available online for download here: http://www.cs.pitt.edu/ chris/politics |
| Dataset Splits | No | The paper mentions training on a 'train set' and evaluating on a 'held-out test set' (75,148 images), but it does not explicitly state a separate validation dataset split with specific numbers or percentages. |
| Hardware Specification | No | The paper does not specify the exact hardware components (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like PyTorch, Doc2Vec (implicitly Gensim), Text Boxes++, and Symspell, but it does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We train all models using Adam [34], with learning rate of 1.0e-4 and minibatch size of 64 images. We use cross-entropy loss and apply class-weight balancing to correct for slight data imbalance between L/R. We use an image size of 224x224 and random horizontal ๏ฌipping as data augmentation. We use Xavier initialization [21] for non-pretrained layers. |