ETNet: Error Transition Network for Arbitrary Style Transfer
Authors: Chunjin Song, Zhijie Wu, Yang Zhou, Minglun Gong, Hui Huang
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Various qualitative and quantitative experiments show that the key concept of both progressive strategy and error-correction leads to better results. Code and models are available at https://github.com/zhijie W94/ETNet. In experiments, our model consistently outperforms the existing state-of-the-art models in qualitative and quantitative evaluations by a large margin. Ablation Study Our method has three key ingredients: iterative refinements, error measurement and the joint analysis between the error features and features of the intermediate stylization. Table 1 lists the quantitative metrics of the ablation study on above ingredients. Quantitative Comparison To quantitatively evaluate each method, we conduct a comparison regarding perceptual loss and report the results in the first two rows of Table 2. |
| Researcher Affiliation | Academia | Chunjin Song Shenzhen University songchunjin1990@gmail.com; Zhijie Wu Shenzhen University wzj.micker@gmail.com; Yang Zhou Shenzhen University zhouyangvcc@gmail.com; Minglun Gong University of Guelph minglun@uoguelph.ca; Hui Huang Shenzhen University hhzhiyan@gmail.com |
| Pseudocode | No | The paper provides architectural diagrams (Figure 2 and Figure 3) and describes the process using equations, but it does not include any structured pseudocode blocks or algorithms. |
| Open Source Code | Yes | Code and models are available at https://github.com/zhijie W94/ETNet. code will be made publicly available online. |
| Open Datasets | No | The paper mentions using content and style images and employs a VGG-based encoder, but it does not explicitly state the names of any public datasets used for training or provide access information for such datasets. It mentions a 'test set' for user studies, but not for training. |
| Dataset Splits | No | The paper refers to a 'test set' for evaluation but does not specify details about training, validation, or test splits (e.g., percentages, sample counts, or a specific splitting methodology). |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using a 'VGG-based encoder' but does not specify any software dependencies (e.g., deep learning frameworks, libraries, or programming languages) with their version numbers. |
| Experiment Setup | Yes | Thus the total loss at k-th level of a pyramid is computed as: Lk total = λk pc Lk pc + λk ps Lk ps + λk tv LT V , (9) where the λk pc and λk tv are always set to 1 and 10 6 while for k = 1, 2, 3, λk ps is assigned to 1, 5, 8 respectively to preserve semantic structures and gradually add style details to outputs. |