Notice: 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. Full accuracy metrics and methodology are described in [1].
OT-DETECTOR: Delving into Optimal Transport for Zero-shot Out-of-Distribution Detection
Authors: Yu Liu, Hao Tang, Haiqi Zhang, Jing Qin, Zechao Li
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on several benchmarks demonstrate that OT-DETECTOR achieves state-of-the-art performance across various OOD detection tasks, particularly in challenging hard-OOD scenarios. |
| Researcher Affiliation | Academia | Yu Liu1 , Hao Tang2 , Haiqi Zhang1 , Jing Qin2 and Zechao Li1, 1School of Computer Science and Engineering, Nanjing University of Science and Technology 2Centre for Smart Health, The Hong Kong Polytechnic University EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Semantic-aware Content Refinement Input : Input image x, ID labels Yid, Image encoder I, Text encoder T , Confidence function M, Top-k selection parameter k, Number of views N Output: Refined feature f r |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Following the previous work [Ming et al., 2022], we evaluate our method on the Image Net-1K OOD benchmark to ensure comparability with other methods. The Image Net-1K OOD benchmark uses Image Net-1K as the ID data and considers Texture [Cimpoi et al., 2014], i Naturalist [Horn et al., 2018], SUN [Xiao et al., 2010], and Places365 [Zhou et al., 2018] as OOD data. |
| Dataset Splits | Yes | Following the previous work [Ming et al., 2022], we evaluate our method on the Image Net-1K OOD benchmark to ensure comparability with other methods. The Image Net-1K OOD benchmark uses Image Net-1K as the ID data and considers Texture [Cimpoi et al., 2014], i Naturalist [Horn et al., 2018], SUN [Xiao et al., 2010], and Places365 [Zhou et al., 2018] as OOD data. Additionally, we perform hard OOD analysis using semantically similar subsets of Image Net constructed by MCM, i.e., Image Net-10, Image Net-20, and Image Net-100, which show high semantic similarity. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'CLIP' and 'CLIP-B/16' as the pretrained model and backbone, and refers to 'Open AI s open-source models', but does not provide specific version numbers for any software dependencies like Python, deep learning frameworks, or libraries. |
| Experiment Setup | Yes | For view augmentation, N = 256 randomly cropped images are generated, and the Top-k = 20 crop features are selected for fusion. In the OT component, we fix ϵ = 90 for entropic regularization and dynamically determine the optimal α for each OOD dataset. |