We lifted 13 functions out of this paper's own repositories and ran 10 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 |
|---|---|---|
| shepnerd/inpainting_gmcnn | canonical | 9 of 12 |
| tlatkowski/inpainting-gmcnn-keras | reimplementation | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| f2uint | Ran | shepnerd/inpainting_gmcnn/tensorflow/util/util.py code served (permissive licence) · get_code("e6bbab52d3ddea75") |
| gauss_kernel | Ran | shepnerd/inpainting_gmcnn/tensorflow/net/ops.py code served (permissive licence) · get_code("c5bc38ee9334ced5") |
| gauss_kernel | Ran | shepnerd/inpainting_gmcnn/pytorch/util/utils.py code served (permissive licence) · get_code("23913d3031134cec") |
| generate_mask_rect | Ran | shepnerd/inpainting_gmcnn/tensorflow/util/util.py code served (permissive licence) · get_code("b1331905adc5865e") |
| generate_rect_mask | Ran | shepnerd/inpainting_gmcnn/pytorch/util/utils.py code served (permissive licence) · get_code("1ea87123e59cd723") |
| gradient_penalty | Ran | shepnerd/inpainting_gmcnn/pytorch/model/loss.py code served (permissive licence) · get_code("685b99c187056ddf") |
| init_net | Ran | shepnerd/inpainting_gmcnn/pytorch/model/layer.py code served (permissive licence) · get_code("8f8e91beb67bca00") |
| l2normalize | Ran | shepnerd/inpainting_gmcnn/pytorch/model/layer.py code served (permissive licence) · get_code("bedff51745d2cf84") |
| postprocess_image | Ran | tlatkowski/inpainting-gmcnn-keras/predict.py code served (permissive licence) · get_code("f94f52013566fd4f") |
| random_interpolate | Ran | shepnerd/inpainting_gmcnn/pytorch/model/loss.py code served (permissive licence) · get_code("587edd4f057b0cb4") |
| free_form_mask_tf | Not yet run | shepnerd/inpainting_gmcnn/tensorflow/net/ops.py code served (permissive licence) · get_code("1959726f4defa0e2") |
| np_free_form_mask | Not yet run | shepnerd/inpainting_gmcnn/pytorch/util/utils.py code served (permissive licence) · get_code("e32ed92be303826d") |
| np_free_form_mask | Not yet run | shepnerd/inpainting_gmcnn/tensorflow/net/ops.py code served (permissive licence) · get_code("0c044ee49bfaec49") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this paper, we propose a generative multi-column network for image inpainting. This network synthesizes different image components in a parallel manner within one stage. To better characterize global structures, we design a confidence-driven reconstruction loss while an implicit diversified MRF regularization is adopted to enhance local details. The multi-column network combined with the reconstruction and MRF loss propagates local and global information derived from context to the target inpainting regions. Extensive experiments on challenging street view, face, natural objects and scenes manifest that our method produces visual compelling results even without previously common post-processing.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1810.08771")
get_code_for_paper("1810.08771")
have("1810.08771")
Connect an agent — have() is free.