DENet: Disentangled Embedding Network for Visible Watermark Removal
Authors: Ruizhou Sun, Yukun Su, Qingyao Wu
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on different challenging benchmarks. Experimental evaluations show that our approach can achieve state-of-the-art performance and yield high-quality images. |
| Researcher Affiliation | Academia | 1School of Software Engineering, South China University of Technology 2Key Laboratory of Big Data and Intelligent Robot, Ministry of Education 3Pazhou Lab, Guangzhou, China 4Peng Cheng Laboratory, China |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks were found in the paper. |
| Open Source Code | Yes | The code is available at: https://github.com/lianchengmingjue/DENet. |
| Open Datasets | Yes | Similar to the existing watermark removal method (Cun and Pun 2021), all the experiments are conducted on the LOGO series dataset. |
| Dataset Splits | No | The paper specifies training (12151 images) and testing (2025 images) splits for the LOGO series datasets, but does not explicitly mention a separate validation dataset split. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running experiments were provided in the paper. |
| Software Dependencies | No | The paper states 'Our method is implemented with Pytorch (Paszke et al. 2019)' but does not provide a specific version number for Pytorch or any other software dependencies. |
| Experiment Setup | Yes | The models are trained for 200 epochs, where the input image resolution is 256 256. We choose Adam (Kingma and Ba 2014) as optimizer with learning rate of 1e-3, batch size 16. The hyper-parameters in (7) are λmask = 1, λvgg = 0.25, λcontrast = 0.25, respectively. Therefore, we set temperature parameter τ to 0.07 and multi-head numbers h to 4 in the following experiments. |