We lifted 3 functions out of this paper's own repositories and ran 2 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 |
|---|---|---|
| copy not recorded | — | 2 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| conv_bn | Ran | this paper's copy was not recorded; identical code first harvested from DingXiaoH/RepMLP pointer only · get_code("7186655759897af6") |
| conv_bn_relu | Ran | this paper's copy was not recorded; identical code first harvested from DingXiaoH/RepMLP pointer only · get_code("18483b4d73618baa") |
| fuse_bn | Not yet run | this paper's copy was not recorded; identical code first harvested from DingXiaoH/RepMLP pointer only · get_code("c1e5b17d52a4eaa8") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Compared to convolutional layers, fully-connected (FC) layers are better at modeling the long-range dependencies but worse at capturing the local patterns, hence usually less favored for image recognition. In this paper, we propose a methodology, Locality Injection, to incorporate local priors into an FC layer via merging the trained parameters of a parallel conv kernel into the FC kernel. Locality Injection can be viewed as a novel Structural Re-parameterization method since it equivalently converts the structures via transforming the parameters. Based on that, we propose a multi-layer-perceptron (MLP) block named RepMLP Block, which uses three FC layers to extract features, and a novel architecture named RepMLPNet. The hierarchical design distinguishes RepMLPNet from the other concurrently proposed vision MLPs. As it produces feature maps of different levels, it qualifies as a backbone model for downstream tasks like semantic segmentation. Our results reveal that 1) Locality Injection is a general methodology for MLP models; 2) RepMLPNet has favorable accuracy-efficiency trade-off compared to the other MLPs; 3) RepMLPNet is the first MLP that seamlessly transfer to Cityscapes semantic segmentation. The code and models are available at https://github.com/DingXiaoH/RepMLP.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2112.11081")
get_code_for_paper("2112.11081")
have("2112.11081")
Connect an agent — have() is free.