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..
EFANet: Exchangeable Feature Alignment Network for Arbitrary Style Transfer
Authors: Zhijie Wu, Chunjin Song, Yang Zhou, Minglun Gong, Hui Huang12305-12312
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Qualitative and quantitative experiments demonstrate the advantages of our approach. |
| Researcher Affiliation | Academia | Zhijie Wu,1 Chunjin Song,1 Yang Zhou,1 Minglun Gong,2 Hui Huang1 1Shenzhen University, 2University of Guelph |
| Pseudocode | No | The paper describes the architecture and processes but does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | Place365 database (Zhou et al. 2014) and Wi Ki Art dataset (Nichol 2016) are used for content and style images respectively, following (Sanakoyeu et1 al. 2018). |
| Dataset Splits | No | The paper describes the training data preparation ('randomly sampled patches of size 256 256') and mentions '100 test images' but does not specify a distinct validation split or explicit percentages for train/validation/test splits. |
| Hardware Specification | Yes | All results are obtained with a 12G Titan V GPU and averaged over 100 256 256 test images. |
| Software Dependencies | No | The paper states 'We implement our model with Tensor๏ฌow (Abadi et al. 2016)' but does not provide specific version numbers for TensorFlow or other software dependencies. |
| Experiment Setup | Yes | We choose Adam optimizer (Kingma and Ba 2014) with a batch size of 4 and a learning rate of 0.0001, and set the decay rates by default for 150000 iterations. and where the four weighting parameters are respectively set as 1, 7, 0.1 and 5 through out the experiments. |