We lifted 11 functions out of this paper's own repositories and ran 8 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| xuekt98/bbdm | canonical | 1 of 1 |
| egshkim/ConditionalBBDM-for-VHR-SAR-to-Optical | pwc_unofficial | 7 of 10 |
| Function | Status | Where it lives |
|---|---|---|
| cal_min_max_coord | Ran | egshkim/ConditionalBBDM-for-VHR-SAR-to-Optical/MSAW_processing.py code served (permissive licence) · get_code("57b76a7beaec87ee") |
| count_zero | Ran | egshkim/ConditionalBBDM-for-VHR-SAR-to-Optical/MSAW_processing.py code served (permissive licence) · get_code("67d8a8f2041657f8") |
| default | Ran | egshkim/ConditionalBBDM-for-VHR-SAR-to-Optical/model/utils.py code served (permissive licence) · get_code("fbf9ec7be545688e") |
| disabled_train | Ran | xuekt98/bbdm/model/BrownianBridge/LatentBrownianBridgeModel.py code served (permissive licence) · get_code("4cb732f513d69dfd") |
| exists | Ran | egshkim/ConditionalBBDM-for-VHR-SAR-to-Optical/model/utils.py code served (permissive licence) · get_code("608e364a9d2376a3") |
| get_image_paths_from_dir | Ran | egshkim/ConditionalBBDM-for-VHR-SAR-to-Optical/datasets/utils.py code served (permissive licence) · get_code("640f637684e6bc4a") |
| get_obj_from_str | Ran | egshkim/ConditionalBBDM-for-VHR-SAR-to-Optical/utils.py code served (permissive licence) · get_code("221b2d116fdf1032") |
| get_tile_name | Ran | egshkim/ConditionalBBDM-for-VHR-SAR-to-Optical/MSAW_processing.py code served (permissive licence) · get_code("12c8467ea13abb37") |
| dict2namespace | Not yet run | egshkim/ConditionalBBDM-for-VHR-SAR-to-Optical/utils.py code served (permissive licence) · get_code("ed80de5f9e0f83b2") |
| extract | Not yet run | egshkim/ConditionalBBDM-for-VHR-SAR-to-Optical/model/utils.py code served (permissive licence) · get_code("09c8479d9a5b3e06") |
| namespace2dict | Not yet run | egshkim/ConditionalBBDM-for-VHR-SAR-to-Optical/utils.py code served (permissive licence) · get_code("b2c2dcaa0d359655") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Image-to-image translation is an important and challenging problem in computer vision and image processing. Diffusion models (DM) have shown great potentials for high-quality image synthesis, and have gained competitive performance on the task of image-to-image translation. However, most of the existing diffusion models treat image-to-image translation as conditional generation processes, and suffer heavily from the gap between distinct domains. In this paper, a novel image-to-image translation method based on the Brownian Bridge Diffusion Model (BBDM) is proposed, which models image-to-image translation as a stochastic Brownian bridge process, and learns the translation between two domains directly through the bidirectional diffusion process rather than a conditional generation process. To the best of our knowledge, it is the first work that proposes Brownian Bridge diffusion process for image-to-image translation. Experimental results on various benchmarks demonstrate that the proposed BBDM model achieves competitive performance through both visual inspection and measurable metrics.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2205.07680")
get_code_for_paper("2205.07680")
have("2205.07680")
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