We lifted 10 functions out of this paper's own repositories and ran 6 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 |
|---|---|---|
| madrylab/smoothed-vit | canonical | 6 of 10 |
| Function | Status | Where it lives |
|---|---|---|
| ablate | Ran | madrylab/smoothed-vit/src/utils/smoothing.py code served (permissive licence) · get_code("9f7dbcd11d378c8d") |
| ablate2 | Ran | madrylab/smoothed-vit/src/utils/smoothing.py code served (permissive licence) · get_code("66c46159973cca31") |
| drop_block_2d | Ran | madrylab/smoothed-vit/src/utils/custom_models/layers/drop.py code served (permissive licence) · get_code("33efc9f1ccc7933c") |
| drop_block_fast_2d | Ran | madrylab/smoothed-vit/src/utils/custom_models/layers/drop.py code served (permissive licence) · get_code("30d63ccefb97a166") |
| drop_path | Ran | madrylab/smoothed-vit/src/utils/custom_models/layers/drop.py code served (permissive licence) · get_code("3ac6b7d76e8e3584") |
| trunc_normal_ | Ran | madrylab/smoothed-vit/src/utils/custom_models/layers/weight_init.py code served (permissive licence) · get_code("02566da69866c48c") |
| certify | Not yet run | madrylab/smoothed-vit/src/utils/smoothing.py code served (permissive licence) · get_code("5ec101dbc1fa66d0") |
| vit_base_patch16_224 | Not yet run | madrylab/smoothed-vit/src/utils/custom_models/vision_transformer.py code served (permissive licence) · get_code("b5560ea79c839c1d") |
| vit_base_patch16_384 | Not yet run | madrylab/smoothed-vit/src/utils/custom_models/vision_transformer.py code served (permissive licence) · get_code("660471bd5729d3b0") |
| vit_small_patch16_224 | Not yet run | madrylab/smoothed-vit/src/utils/custom_models/vision_transformer.py code served (permissive licence) · get_code("9fd5a755657a7d0b") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Certified patch defenses can guarantee robustness of an image classifier to arbitrary changes within a bounded contiguous region. But, currently, this robustness comes at a cost of degraded standard accuracies and slower inference times. We demonstrate how using vision transformers enables significantly better certified patch robustness that is also more computationally efficient and does not incur a substantial drop in standard accuracy. These improvements stem from the inherent ability of the vision transformer to gracefully handle largely masked images. Our code is available at https://github.com/MadryLab/smoothed-vit.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2110.07719")
get_code_for_paper("2110.07719")
have("2110.07719")
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