Si-Hyeon Lee, Won-Jun Jang, Hyeon-Seo Park
We lifted 22 functions out of this paper's own repositories and ran 16 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 |
|---|---|---|
| autonomousvision/stylegan-xl | canonical | 14 of 20 |
| kuangliu/pytorch-cifar | canonical | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| parse_tuple | Ran | autonomousvision/stylegan-xl/dataset_tool.py code served (permissive licence) · get_code("9cd07419f7c926f8") |
| NormLayer | Ran | autonomousvision/stylegan-xl/pg_modules/blocks.py code served (permissive licence) · get_code("ef1e7dffb5d433d4") |
| UpBlockBig | Ran | autonomousvision/stylegan-xl/pg_modules/blocks.py code served (permissive licence) · get_code("f87cd99314206f72") |
| UpBlockSmall | Ran | autonomousvision/stylegan-xl/pg_modules/blocks.py code served (permissive licence) · get_code("0186912dcb8b92b9") |
| drop_connect | Ran | kuangliu/pytorch-cifar/models/efficientnet.py code served (permissive licence) · get_code("4304a326c593f8db") |
| file_ext | Ran | autonomousvision/stylegan-xl/dataset_tool.py code served (permissive licence) · get_code("a2b45afa097b55b6") |
| forward_flex | Ran | autonomousvision/stylegan-xl/feature_networks/vit.py code served (permissive licence) · get_code("562cccfa5027f008") |
| get_activation | Ran | autonomousvision/stylegan-xl/feature_networks/vit.py code served (permissive licence) · get_code("1c094193a4506a40") |
| layout_grid | Ran | autonomousvision/stylegan-xl/gen_video.py code served (permissive licence) · get_code("456c172851946697") |
| make_transform | Ran | autonomousvision/stylegan-xl/gen_images.py code served (permissive licence) · get_code("f47dff4b7b27bba7") |
| maybe_min | Ran | autonomousvision/stylegan-xl/dataset_tool.py code served (permissive licence) · get_code("dd1dc700e87f36cc") |
| parse_comma_separated_list | Ran | autonomousvision/stylegan-xl/calc_metrics.py code served (permissive licence) · get_code("d01b68a634f71551") |
| parse_range | Ran | autonomousvision/stylegan-xl/gen_images.py code served (permissive licence) · get_code("236de8614a178382") |
| parse_range | Ran | autonomousvision/stylegan-xl/gen_video.py code served (permissive licence) · get_code("025e4c7cda618f25") |
| parse_vec2 | Ran | autonomousvision/stylegan-xl/gen_images.py code served (permissive licence) · get_code("180016b9227381b1") |
| swish | Ran | kuangliu/pytorch-cifar/models/efficientnet.py code served (permissive licence) · get_code("8737c82de631cffc") |
| ask_yes_no | Not yet run | autonomousvision/stylegan-xl/dnnlib/util.py code served (permissive licence) · get_code("9d31d2c4cd16bb2d") |
| compute_is | Not yet run | autonomousvision/stylegan-xl/metrics/inception_score.py code served (permissive licence) · get_code("fc49fa553c005268") |
| format_time | Not yet run | autonomousvision/stylegan-xl/dnnlib/util.py code served (permissive licence) · get_code("053fc534bc6bb989") |
| format_time_brief | Not yet run | autonomousvision/stylegan-xl/dnnlib/util.py code served (permissive licence) · get_code("77f4aa0649e7f404") |
| forward_vit | Not yet run | autonomousvision/stylegan-xl/feature_networks/vit.py code served (permissive licence) · get_code("4c52162336ee4320") |
| parse_tuple | Not yet run | autonomousvision/stylegan-xl/gen_video.py code served (permissive licence) · get_code("6a0f2095bdd516ab") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Federated ensemble distillation addresses client heterogeneity by generating pseudo-labels for an unlabeled server dataset based on client predictions and training the server model using the pseudo-labeled dataset. The unlabeled server dataset can either be pre-existing or generated through a data-free approach. The effectiveness of this approach critically depends on the method of assigning weights to client predictions when creating pseudo-labels, especially in highly heterogeneous settings. Inspired by theoretical results from GANs, we propose a provably near-optimal weighting method that leverages client discriminators trained with a server-distributed generator and local datasets. Our experiments on various image classification tasks demonstrate that the proposed method significantly outperforms baselines. Furthermore, we show that the additional communication cost, client-side privacy leakage, and client-side computational overhead introduced by our method are negligible, both in scenarios with and without a pre-existing server dataset.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2502.06349")
get_code_for_paper("2502.06349")
have("2502.06349")
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