Kernel PCA for Out-of-Distribution Detection
Authors: Kun Fang, Qinghua Tao, Kexin Lv, Mingzhen He, Xiaolin Huang, JIE YANG
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive theoretical and empirical results on multiple Oo D data sets and network structures verify the superiority of our KPCA detector in efficiency and efficacy with state-of-the-art detection performance. Extensive experiments verify the effectiveness of the devoted two kernels. |
| Researcher Affiliation | Collaboration | Kun Fang1 Qinghua Tao2 Kexin Lv3 Mingzhen He1 Xiaolin Huang1 Jie Yang1 1Shanghai Jiao Tong University 2ESAT-STADIUS, KU Leuven 3China Mobile Shanghai ICT Co., Ltd |
| Pseudocode | Yes | Algorithm 1 Kernel PCA for Out-of-Distribution Detection |
| Open Source Code | Yes | The source code of this work has been publicly released1. 1https://github.com/fanghenshaometeor/ood-kernel-pca |
| Open Datasets | Yes | Datasets Experiments are executed on the commonly-used small-scale CIFAR10 [39] and large-scale Image Net-1K benchmarks [40], following the settings in [7, 8]. |
| Dataset Splits | Yes | Datasets Experiments are executed on the commonly-used small-scale CIFAR10 [39] and large-scale Image Net-1K benchmarks [40], following the settings in [7, 8]. Following the setups in KNN, for fair comparisons, we evaluate models trained via the standard cross-entropy loss and models trained via the supervised contrastive learning [49], and adopt the same checkpoints released by KNN: Res Net18 [50] on CIFAR10 and Res Net50 on Image Net-1K. |
| Hardware Specification | Yes | All the experiments are executed on 1 NVIDIA Ge Force RTX 3090 GPU. |
| Software Dependencies | No | The detection experiments are executed on the Image Net-1K benchmark with pre-trained Res Net50 and Mobile Net [51] checkpoints from Py Torch [52]. |
| Experiment Setup | Yes | Following the setups in KNN, for fair comparisons, we evaluate models trained via the standard cross-entropy loss and models trained via the supervised contrastive learning [49], and adopt the same checkpoints released by KNN: Res Net18 [50] on CIFAR10 and Res Net50 on Image Net-1K. in the comparison experiments, we adopt M = 4m on CIFAR10 with m = 512 for Res Net18, and M = 2m on Image Net-1K with m = 2048 for Res Net50 and m = 1280 for Mobile Net. A common hyper-parameter in Co P and Co RP is the number of columns q of the dimensionality-reduction matrix U Φ q . Additional hyper-parameters of Co RP include the bandwidth γ of the Gaussian kernel and the number of RFFs M. |