Detecting Out-of-distribution Data through In-distribution Class Prior
Authors: Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that both strategies can improve the OOD detection performance when the ID model is pre-trained with imbalanced data, reflecting the importance of ID-class prior in OOD detection. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, Southern University of Science and Technology 2Department of Computer Science, Hong Kong Baptist University 3University of Melbourne 4Australian Artificial Intelligence Institute, University of Technology Sydney 5College of Informatics, Huazhong Agricultural University 6Mohamed bin Zayed University of Artificial Intelligence 7Sydney AI Centre, The University of Sydney. |
| Pseudocode | No | The paper does not contain clearly labeled algorithm sections or code-like formatted procedures (pseudocode). |
| Open Source Code | Yes | The codes are available at https://github. com/tmlr-group/class_prior. |
| Open Datasets | Yes | We construct a series of imbalanced ID datasets that are sampled by the Pareto distribution in Image Net-1K dataset. (Deng et al., 2009) ... the OOD datasets include the subsets of i Naturalist (Horn et al., 2018), SUN (Xiao et al., 2010), Places (Zhou et al., 2018), and Textures (Cimpoi et al., 2014). |
| Dataset Splits | No | The paper discusses training and test data, but it does not provide specific train/validation/test dataset splits (e.g., percentages, sample counts, or explicit references to predefined splits for reproduction). |
| Hardware Specification | Yes | Note that, all methods are realized by Pytorch 1.6.0 with CUDA 10.2, where we use several NVIDIA Tesla V100 GPUs. |
| Software Dependencies | Yes | We use mmclassification3 (Contributors, 2020) with Apache-2.0 license to train ID models. Note that, all methods are realized by Pytorch 1.6.0 with CUDA 10.2, where we use several NVIDIA Tesla V100 GPUs. |
| Experiment Setup | No | The training details of Res Net (He et al., 2016) and Mobile Net (Howard et al., 2019) follow the default setting in mmclassification. |