Exploring Channel-Aware Typical Features for Out-of-Distribution Detection
Authors: Rundong He, Yue Yuan, Zhongyi Han, Fan Wang, Wan Su, Yilong Yin, Tongliang Liu, Yongshun Gong
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments verify the effectiveness and generalization of LAPS under different architectures and OOD scores. |
| Researcher Affiliation | Academia | 1Shandong University 2 Mohamed bin Zayed University of Artificial Intelligence 3 The University of Sydney |
| Pseudocode | No | The paper describes the proposed method using mathematical equations and textual explanations, but it does not include a formal pseudocode block or algorithm. |
| Open Source Code | Yes | Code is available at: https://github.com/rm1972/LAPS.git. |
| Open Datasets | Yes | We use CIFAR-100 (Krizhevsky and Hinton 2009) as ID data for small-scale OOD detection benchmark. |
| Dataset Splits | No | The paper mentions 'training set' and 'test set' but does not explicitly specify the proportions or methodology for validation splits. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers, such as specific programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | LAPS has three important hyper-parameters (including λ, m, and n). Here, we empirically show the influence of the hyperparameter λ in Fig. 5(a). |