Xiaolin Huang, Jie Yang, Qinghua Tao, Kun Fang, Kexin Lv, Mingzhen He
We lifted 5 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| fanghenshaometeor/ood-kpca-extension | — | 2 of 3 |
| copy not recorded | — | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| fpr_and_fdr_at_recall | Ran | this paper's copy was not recorded; identical code first harvested from fanghenshaometeor/ood-kernel-pca-ext pointer only · get_code("e1dfe697d81dc165") |
| iterate_data_react | Ran | fanghenshaometeor/ood-kpca-extension/utils_ood.py pointer only (licence: NONE) · get_code("5dcd5cdb8a08e275") |
| stable_cumsum | Ran | this paper's copy was not recorded; identical code first harvested from fanghenshaometeor/ood-kernel-pca-ext pointer only · get_code("d4acb3120a027622") |
| val_ood_fuse | Ran | fanghenshaometeor/ood-kpca-extension/utils_ood.py pointer only (licence: NONE) · get_code("48e491ddc2c394f5") |
| get_measures | Not yet run | fanghenshaometeor/ood-kpca-extension/utils_ood.py pointer only (licence: NONE) · get_code("914e5f1ab3d0dfdf") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Out-of-Distribution (OoD) detection is vital for the reliability of Deep Neural Networks (DNNs). Existing works have shown the insufficiency of Principal Component Analysis (PCA) straightforwardly applied on the features of DNNs in detecting OoD data from In-Distribution (InD) data. The failure of PCA suggests that the network features residing in OoD and InD are not well separated by simply proceeding in a linear subspace, which instead can be resolved through proper non-linear mappings. In this work, we leverage the framework of Kernel PCA (KPCA) for OoD detection, and seek suitable non-linear kernels that advocate the separability between InD and OoD data in the subspace spanned by the principal components. Besides, explicit feature mappings induced from the devoted taskspecific kernels are adopted so that the KPCA reconstruction error for new test samples can be efficiently obtained with large-scale data. Extensive theoretical and empirical results on multiple OoD data sets and network structures verify the superiority of our KPCA detector in efficiency and efficacy with state-of-the-art detection performance.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2402.02949")
get_code_for_paper("2402.02949")
have("2402.02949")
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