Yi Yu, Song Xia, Xudong Jiang
We lifted 10 functions out of this paper's own repositories and ran 7 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 |
|---|---|---|
| xiasong0501/DRS | canonical | 6 of 9 |
| xiasong0501/drs | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| conv3x3 | Ran | xiasong0501/DRS/archs/cifar_resnet.py pointer only (licence: NONE) · get_code("fac5364e2f53c6db") |
| entropy | Ran | xiasong0501/DRS/imagenet_train_lowres_2.py pointer only (licence: NONE) · get_code("23f822c3d6d64a12") |
| get_low_res_data | Ran | xiasong0501/drs/DDS/imagenet/DRM.py pointer only (licence: NONE) · get_code("a16b11c89a38dfca") |
| get_low_res_data | Ran | xiasong0501/DRS/core1_lowres_2.py pointer only (licence: NONE) · get_code("966d9e557ce5aa94") |
| get_low_res_data | Ran | xiasong0501/DRS/imagenet_train_lowres_2.py pointer only (licence: NONE) · get_code("4d4b1a4a3fc08e85") |
| get_upper_res_data | Ran | xiasong0501/DRS/core1_lowres_2.py pointer only (licence: NONE) · get_code("3cd8ab8f4217978f") |
| kl_div | Ran | xiasong0501/DRS/imagenet_train_lowres_2.py pointer only (licence: NONE) · get_code("74325f9c5d49ca67") |
| dali_data | Not yet run | xiasong0501/DRS/imagenet_model_lower_2.py pointer only (licence: NONE) · get_code("54e10cf0f3dd3389") |
| get_imagenet_iter_torch | Not yet run | xiasong0501/DRS/imagenet_dali.py pointer only (licence: NONE) · get_code("6ddacd69764c3835") |
| get_low_res_data_mn | Not yet run | xiasong0501/DRS/core1_lowres_2.py pointer only (licence: NONE) · get_code("a1abf4153e3f406a") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Randomized Smoothing (RS) has been proven a promising method for endowing an arbitrary image classifier with certified robustness. However, the substantial uncertainty inherent in the high-dimensional isotropic Gaussian noise imposes the curse of dimensionality on RS. Specifically, the upper bound of ℓ 2 certified robustness radius provided by RS exhibits a diminishing trend with the expansion of the input dimension d, proportionally decreasing at a rate of 1/ √ d. This paper explores the feasibility of providing ℓ 2 certified robustness for high-dimensional input through the utilization of dual smoothing in the lower-dimensional space. The proposed Dual Randomized Smoothing (DRS) down-samples the input image into two sub-images and smooths the two sub-images in lower dimensions. Theoretically, we prove that DRS guarantees a tight ℓ 2 certified robustness radius for the original input and reveal that DRS attains a superior upper bound on the ℓ 2 robustness radius, which decreases proportionally at a rate of Extensive experiments demonstrate the generalizability and effectiveness of DRS, which exhibits a notable capability to integrate with established methodologies, yielding substantial improvements in both accuracy and ℓ 2 certified robustness baselines of RS on the CIFAR-10 and ImageNet datasets. Code is available at https://github.com/xiasong0501/DRS.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2404.09586")
get_code_for_paper("2404.09586")
have("2404.09586")
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