Universal Style Transfer via Feature Transforms
Authors: Yijun Li, Chen Fang, Jimei Yang, Zhaowen Wang, Xin Lu, Ming-Hsuan Yang
NeurIPS 2017 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our algorithm by generating high-quality stylized images with comparisons to a number of recent methods. We evaluate the proposed algorithm with existing approaches extensively on both style transfer and texture synthesis tasks and present in-depth analysis. 4 Experimental Results |
| Researcher Affiliation | Collaboration | Yijun Li UC Merced EMAIL; Chen Fang Adobe Research EMAIL; Jimei Yang Adobe Research EMAIL; Zhaowen Wang Adobe Research EMAIL; Xin Lu Adobe Research EMAIL; Ming-Hsuan Yang UC Merced, NVIDIA Research EMAIL |
| Pseudocode | No | The paper includes pipeline diagrams but no explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The models and code are available at https://github.com/Yijunmaverick/Universal Style Transfer. |
| Open Datasets | Yes | It is trained on the Microsoft COCO dataset [22]; [22] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick. Microsoft COCO: Common objects in context. In ECCV, 2014. |
| Dataset Splits | No | The paper states training on the Microsoft COCO dataset but does not provide specific training/validation/test splits, percentages, or sample counts. |
| Hardware Specification | Yes | Quantitative comparisons between different stylization methods in terms of the covariance matrix difference (Ls), user preference and run-time, tested on images of size 256 256 and a 12GB TITAN X. |
| Software Dependencies | No | The paper mentions models and datasets but does not specify any software libraries, frameworks, or their version numbers used for implementation (e.g., PyTorch, TensorFlow, or specific Python versions). |
| Experiment Setup | Yes | The pixel reconstruction loss [5] and feature loss [16, 5] are employed for reconstructing an input image... In addition, λ is the weight to balance the two losses. For the multi-level stylization approach... the weight λ to balance the two losses in (1) is set as 1. After the WCT, we may blend ˆ fcs with the content feature fc as in (4) before feeding it to the decoder... ˆ fcs = α ˆ fcs + (1 α) fc , (4) where α serves as the style weight for users to control the transfer effect. For our results, we set the style weight α = 0.6. |