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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
AdaNeg: Adaptive Negative Proxy Guided OOD Detection with Vision-Language Models
Authors: Yabin Zhang, Lei Zhang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments across various benchmarks demonstrate the effectiveness of our approach, abbreviated as Ada Neg. |
| Researcher Affiliation | Academia | Yabin Zhang The Hong Kong Polytechnic University EMAIL Lei Zhang The Hong Kong Polytechnic University EMAIL |
| Pseudocode | Yes | Algorithm 1 Adaptive Negative Proxy Guided OOD Detection |
| Open Source Code | Yes | Codes are available at https://github.com/YBZh/Open OOD-VLM. |
| Open Datasets | Yes | We conduct extensive experiments with the large-scale Image Net-1k [9] as ID data. Following prior practice [26, 39, 27], four OOD datasets of i Naturalist [60], SUN [68], Places [78], and Textures [7] are evaluated. |
| Dataset Splits | No | The paper describes a training-free method and discusses hyperparameter tuning in Section 4.3, but it does not specify explicit training/validation/test dataset splits in the traditional sense for model validation during a training phase. |
| Hardware Specification | Yes | All experiments are conducted with a single Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions using the visual encoder of VITB/16 pretrained by CLIP [48] but does not provide specific version numbers for software libraries or frameworks like PyTorch, TensorFlow, or Python versions. |
| Experiment Setup | Yes | For hyper-parameters, we adopt the memory length L=10, threshold γ=0.5 with the gap g=0.5 in Eq. 8, β=5.5 in Eq. 11, and λ=0.1 in Eq. 13 in all experiments. |