We lifted 2 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 2 |
| Function | Status | Where it lives |
|---|---|---|
| swin_reshape_transform | Ran | this paper's copy was not recorded; identical code first harvested from frgfm/torch-cam pointer only · get_code("cd9e431963bd28ca") |
| vit_reshape_transform | Ran | this paper's copy was not recorded; identical code first harvested from frgfm/torch-cam pointer only · get_code("3e3e7c2d436361ca") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Interpretation of the underlying mechanisms of Deep Convolutional Neural Networks has become an important aspect of research in the field of deep learning due to their applications in high-risk environments. To explain these black-box architectures there have been many methods applied so the internal decisions can be analyzed and understood. In this paper, built on the top of Score-CAM, we introduce an enhanced visual explanation in terms of visual sharpness called SS-CAM, which produces centralized localization of object features within an image through a smooth operation. We evaluate our method on the ILSVRC 2012 Validation dataset, which outperforms Score-CAM on both faithfulness and localization tasks.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2006.14255")
get_code_for_paper("2006.14255")
have("2006.14255")
Connect an agent — have() is free.