Phillip Isola, Caroline Chan, Frédo Durand
We lifted 18 functions out of this paper's own repositories and ran 17 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 |
|---|---|---|
| carolineec/informative-drawings | canonical | 17 of 18 |
| Function | Status | Where it lives |
|---|---|---|
| add_margin | Ran | carolineec/informative-drawings/base_dataset.py code served (permissive licence) · get_code("2623c33654e1da14") |
| channel2width | Ran | carolineec/informative-drawings/utils.py code served (permissive licence) · get_code("c5f6a77d35d36163") |
| createNRandompatches | Ran | carolineec/informative-drawings/utils.py code served (permissive licence) · get_code("adce554a3dd42bae") |
| get_norm_layer | Ran | carolineec/informative-drawings/networks.py code served (permissive licence) · get_code("30190c576dd53b28") |
| get_scheduler | Ran | carolineec/informative-drawings/networks.py code served (permissive licence) · get_code("78865ef3023c58ad") |
| get_transform | Ran | carolineec/informative-drawings/base_dataset.py code served (permissive licence) · get_code("e045b7099733bf40") |
| gram_matrix | Ran | carolineec/informative-drawings/utils_pl.py code served (permissive licence) · get_code("430c582cd8bbb1df") |
| init_net | Ran | carolineec/informative-drawings/networks.py code served (permissive licence) · get_code("10fdae626d954364") |
| is_image_file | Ran | carolineec/informative-drawings/dataset.py code served (permissive licence) · get_code("59848503040bbdac") |
| load_image | Ran | carolineec/informative-drawings/utils_pl.py code served (permissive licence) · get_code("7d363fa05027abd2") |
| make_dataset | Ran | carolineec/informative-drawings/dataset.py code served (permissive licence) · get_code("5c701194ea1b715a") |
| normalize_batch | Ran | carolineec/informative-drawings/utils_pl.py code served (permissive licence) · get_code("9a49c7512264eb68") |
| tensor2im | Ran | carolineec/informative-drawings/util/util.py code served (permissive licence) · get_code("3c356092464d28e0") |
| tensor2image | Ran | carolineec/informative-drawings/utils.py code served (permissive licence) · get_code("0ee3f9ee92d8a8c6") |
| tensor2imv2 | Ran | carolineec/informative-drawings/util/util.py code served (permissive licence) · get_code("1590350b5aa06dc4") |
| tensor2label | Ran | carolineec/informative-drawings/util/util.py code served (permissive licence) · get_code("60597ae0bacbcfc2") |
| unpickle | Ran | carolineec/informative-drawings/dataset.py code served (permissive licence) · get_code("80b795d920e2967a") |
| get_params | Not yet run | carolineec/informative-drawings/base_dataset.py code served (permissive licence) · get_code("d80e86b2b7fb857b") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Figure 1. Given a set of photographs, our method is capable of making line drawings in different styles seen above. Our method only requires unpaired data during training.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2203.12691")
get_code_for_paper("2203.12691")
have("2203.12691")
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