| license: mit | |
| tags: | |
| - underwater-acoustic | |
| - channel-estimation | |
| - denoising | |
| - deep-learning | |
| # DSS-Net Checkpoints | |
| Pre-trained model checkpoints for **DSS-Net: Dynamic-Static Separation Networks for UWA Channel Denoising**. | |
| ## Available Models | |
| | Model | File | Size | Description | | |
| |-------|------|------|-------------| | |
| | **DSS-Net (Full)** | `dss_net_full_best.pth` | 499MB | Best performing model (NMSE: -25.27 dB) | | |
| | Baseline U-Net | `baseline_unet_best.pth` | 355MB | Baseline for comparison (NMSE: -20.41 dB) | | |
| ## Usage | |
| ```python | |
| import torch | |
| from model import UNetDecomposer | |
| # Load model | |
| model = UNetDecomposer( | |
| in_channels=2, | |
| base_channels=64, | |
| depth=4, | |
| use_attention=True | |
| ) | |
| # Load weights | |
| checkpoint = torch.load('dss_net_full_best.pth', map_location='cpu') | |
| model.load_state_dict(checkpoint['model_state_dict']) | |
| model.eval() | |
| ``` | |
| ## Citation | |
| ```bibtex | |
| @article{yang2025dssnet, | |
| title={DSS-Net: Dynamic--Static Separation Networks for Physics-Inspired UWA Channel Denoising}, | |
| author={Yang, Xiaoyu and Chen, Yinda and Tong, Feng and Zhou, Yuehai}, | |
| journal={IEEE Transactions on Wireless Communications}, | |
| year={2025} | |
| } | |
| ``` | |
| ## Links | |
| - **GitHub**: https://github.com/ydchen0806/dss_net | |
| - **Paper**: IEEE TWC 2025 | |