Nannan Wang, Yuhao Lin, Fei Gao, Gang Xu, Biao Ma, Chang Jiang
We lifted 11 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 |
|---|---|---|
| AiArt-HDU/MATEBIT | — | 10 of 11 |
| Function | Status | Where it lives |
|---|---|---|
| AdaBlock | Ran | AiArt-HDU/MATEBIT/models/networks/dynast_transformer.py code served (permissive licence) · get_code("46185d38c66d915f") |
| CoordAtt | Ran | AiArt-HDU/MATEBIT/models/networks/dynast_transformer.py code served (permissive licence) · get_code("a364013be1afea2e") |
| LayerNorm | Ran | AiArt-HDU/MATEBIT/models/networks/dynast_transformer.py code served (permissive licence) · get_code("91d27ae48f10c634") |
| PositionalNorm2d | Ran | AiArt-HDU/MATEBIT/models/networks/dynast_transformer.py code served (permissive licence) · get_code("cabca479cad0531f") |
| SPADE | Ran | AiArt-HDU/MATEBIT/models/networks/dynast_transformer.py code served (permissive licence) · get_code("93da98f4f6845b01") |
| SignWithSigmoidGrad | Ran | AiArt-HDU/MATEBIT/models/networks/dynast_transformer.py code served (permissive licence) · get_code("70bbb1a234bbec73") |
| dynamic_attention | Ran | AiArt-HDU/MATEBIT/models/networks/dynast_transformer.py code served (permissive licence) · get_code("fd6117bfc579efa5") |
| feature_normalize | Ran | AiArt-HDU/MATEBIT/models/networks/dynast_transformer.py code served (permissive licence) · get_code("2ab25c1da9832507") |
| h_sigmoid | Ran | AiArt-HDU/MATEBIT/models/networks/dynast_transformer.py code served (permissive licence) · get_code("7e179a91bb50201f") |
| h_swish | Ran | AiArt-HDU/MATEBIT/models/networks/dynast_transformer.py code served (permissive licence) · get_code("5e356c9d5f8f95df") |
| DynamicTransformerBlock | Not yet run | AiArt-HDU/MATEBIT/models/networks/dynast_transformer.py code served (permissive licence) · get_code("77eb59f07e359be1") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We present a novel framework for exemplar based image translation. Recent advanced methods for this task mainly focus on establishing cross-domain semantic correspondence, which sequentially dominates image generation in the manner of local style control. Unfortunately, crossdomain semantic matching is challenging; and matching errors ultimately degrade the quality of generated images. To overcome this challenge, we improve the accuracy of matching on the one hand, and diminish the role of matching in image generation on the other hand. To achieve the former, we propose a masked and adaptive transformer (MAT) for learning accurate cross-domain correspondence, and executing context-aware feature augmentation. To achieve the latter, we use source features of the input and global style codes of the exemplar, as supplementary information, for decoding an image. Besides, we devise a novel contrastive style learning method, for acquire quality-discriminative style representations, which in turn benefit high-quality image generation. Experimental results show that our method, dubbed MATEBIT, performs considerably better than state-of-the-art methods, in diverse image translation tasks. The codes are available at https://github.com/AiArt-HDU/MATEBIT.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2303.17123")
get_code_for_paper("2303.17123")
have("2303.17123")
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