EFANet: Exchangeable Feature Alignment Network for Arbitrary Style Transfer
Authors: Zhijie Wu, Chunjin Song, Yang Zhou, Minglun Gong, Hui Huang12305-12312
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | 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 Tensorflow (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. |