From Reality to Perception: Genre-Based Neural Image Style Transfer
Authors: Zhuoqi Ma, Nannan Wang, Xinbo Gao, Jie Li
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results demonstrate that our method can capture the overall style of a genre or an artist. We conducted our experiments on our Van Gogh-photo dataset and the cubism-photo dataset. |
| Researcher Affiliation | Academia | Zhuoqi Ma , Nannan Wang , Xinbo Gao , Jie Li State Key Laboratory of Integrated Services Networks, School of Electronic Engineering, Xidian University, Xi an 710071, China State Key Laboratory of Integrated Services Networks, School of Telecommunications, Xidian University, Xi an 710071, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access (specific repository link, explicit code release statement, or code in supplementary materials) to the source code for the methodology described. |
| Open Datasets | No | We conducted our experiments on our Van Gogh-photo dataset and the cubism-photo dataset. The Van Gogh-photo dataset consists of 309 Van Goghs painting and the semantically corresponding real world photo pairs. The cubism-photo dataset has 34 cubist painting and real world photo pairs. No concrete access information (specific link, DOI, repository name, formal citation, or reference to established benchmark datasets) for a publicly available or open dataset is provided. |
| Dataset Splits | No | The paper mentions training data ("All the paintings and the photos is scaled to the size of 256x256. We train the network with a batch size of 5 for 300 epochs."), but does not provide specific dataset split information for validation, such as percentages, sample counts, or details on how data was partitioned for validation. |
| Hardware Specification | Yes | All experiments are conducted with Python on Ubuntu 16.04 system, with i7-4790 3.6G CPU and 12G NVIDIA Titan X GPU. |
| Software Dependencies | No | The paper mentions 'Python' and 'Ubuntu 16.04 system', but does not provide specific version numbers for key ancillary software dependencies such as deep learning frameworks (e.g., TensorFlow, PyTorch), libraries, or specific solvers. |
| Experiment Setup | Yes | The network is trained on the paintings of separate genres. All the paintings and the photos is scaled to the size of 256 x 256. We train the network with a batch size of 5 for 300 epochs. And we adopt the Adam optimization method [Kingma and Ba, 2015] with the initial learning rate of 0.001. We do not apply weight decay as the model would not easily overfit. |