Fanghui Liu, Volkan Cevher, Elias Rocamora
We lifted 2 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| megaelius/pnverification | canonical | 1 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| get_norm | Ran | megaelius/pnverification/utils_PN.py code served (permissive licence) · get_code("43a210cd6de06fe2") |
| from_CCP_Conv_to_CCP | Not yet run | megaelius/pnverification/utils_PN.py code served (permissive licence) · get_code("58688cbe43a461a3") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Polynomial Networks (PNs) have demonstrated promising performance on face and image recognition recently. However, robustness of PNs is unclear and thus obtaining certificates becomes imperative for enabling their adoption in real-world applications. Existing verification algorithms on ReLU neural networks (NNs) based on classical branch and bound (BaB) techniques cannot be trivially applied to PN verification. In this work, we devise a new bounding method, equipped with BaB for global convergence guarantees, called Verification of Polynomial Networks or VPN for short. One key insight is that we obtain much tighter bounds than the interval bound propagation (IBP) and DeepT-Fast [Bonaert et al., 2021] baselines. This enables sound and complete PN verification with empirical validation on MNIST, CIFAR10 and STL10 datasets. We believe our method has its own interest to NN verification. The source code is publicly available at https://github.com/megaelius/PNVerification.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2209.07235")
get_code_for_paper("2209.07235")
have("2209.07235")
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