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..
A Learned Representation For Artistic Style
Authors: Vincent Dumoulin, Jonathon Shlens, Manjunath Kudlur
ICLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this work we investigate the construction of a single, scalable deep network that can parsimoniously capture the artistic style of a diversity of paintings. We demonstrate that such a network generalizes across a diversity of artistic styles by reducing a painting to a point in an embedding space. Importantly, this model permits a user to explore new painting styles by arbitrarily combining the styles learned from individual paintings. |
| Researcher Affiliation | Industry | Vincent Dumoulin & Jonathon Shlens & Manjunath Kudlur Google Brain, Mountain View, CA EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain a structured pseudocode or algorithm block. Figure 3 shows mathematical equations, not pseudocode. |
| Open Source Code | Yes | A complete implementation of the model in Tensor Flow (Abadi et al., 2016) as well as a pretrained model are available for download 1. 1https://github.com/tensorflow/magenta |
| Open Datasets | Yes | Our training procedure follows Johnson et al. (2016). Briefly, we employ the Image Net dataset (Deng et al., 2009) as a corpus of training content images. |
| Dataset Splits | No | The paper mentions 'evaluation images' but does not specify explicit training, validation, and test dataset splits with percentages or counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Tensor Flow' but does not provide a specific version number for the software used in their implementation (e.g., TensorFlow 1.x or 2.x). |
| Experiment Setup | Yes | Unless noted otherwise, all style transfer networks were trained using the hyperparameters outlined in the Appendix s Table 1. ... Optimizer Adam (Kingma & Ba, 2014) (α = 0.001, β1 = 0.9, β2 = 0.999), Parameter updates 40,000, Batch size 16, Weight initialization Isotropic gaussian (µ = 0, σ = 0.01) |