Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
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 | Venue PDF | LLM Run Details | Input Tokens: 18,470 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,434 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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). |