We lifted 6 functions out of this paper's own repositories and ran 5 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 |
|---|---|---|
| alinlab/smoothing-catrs | canonical | 5 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| consistency_loss | Ran | alinlab/smoothing-catrs/code/consistency.py code served (permissive licence) · get_code("3d45147f0a674bc0") |
| conv3x3 | Ran | alinlab/smoothing-catrs/code/archs/cifar_resnet.py code served (permissive licence) · get_code("fac5364e2f53c6db") |
| entropy | Ran | alinlab/smoothing-catrs/code/consistency.py code served (permissive licence) · get_code("23f822c3d6d64a12") |
| get_num_classes | Ran | alinlab/smoothing-catrs/code/datasets.py code served (permissive licence) · get_code("ddca6b8fb149d7f9") |
| kl_div | Ran | alinlab/smoothing-catrs/code/consistency.py code served (permissive licence) · get_code("6518bcfd499a3632") |
| get_dataset | Not yet run | alinlab/smoothing-catrs/code/datasets.py code served (permissive licence) · get_code("8a021efe12dc7d06") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Any classifier can be "smoothed out" under Gaussian noise to build a new classifier that is provably robust to $\ell_2$-adversarial perturbations, viz., by averaging its predictions over the noise via randomized smoothing. Under the smoothed classifiers, the fundamental trade-off between accuracy and (adversarial) robustness has been well evidenced in the literature: i.e., increasing the robustness of a classifier for an input can be at the expense of decreased accuracy for some other inputs. In this paper, we propose a simple training method leveraging this trade-off to obtain robust smoothed classifiers, in particular, through a sample-wise control of robustness over the training samples. We make this control feasible by using "accuracy under Gaussian noise" as an easy-to-compute proxy of adversarial robustness for an input. Specifically, we differentiate the training objective depending on this proxy to filter out samples that are unlikely to benefit from the worst-case (adversarial) objective. Our experiments show that the proposed method, despite its simplicity, consistently exhibits improved certified robustness upon state-of-the-art training methods. Somewhat surprisingly, we find these improvements persist even for other notions of robustness, e.g., to various types of common corruptions.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2212.09000")
get_code_for_paper("2212.09000")
have("2212.09000")
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