AdaNeg: Adaptive Negative Proxy Guided OOD Detection with Vision-Language Models
Authors: Yabin Zhang, Lei Zhang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 csybzhang@comp.polyu.edu.hk Lei Zhang The Hong Kong Polytechnic University cslzhang@comp.polyu.edu.hk |
| 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. |