Category-Extensible Out-of-Distribution Detection via Hierarchical Context Descriptions
Authors: Kai Liu, Zhihang Fu, Chao Chen, Sheng Jin, Ze Chen, Mingyuan Tao, Rongxin Jiang, Jieping Ye
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to show the proposed hierarchical context descriptions are crucial to precisely and universally define each category. As a result, our method consistently outperforms the competitors on the large-scale OOD datasets, while showing comparable or even better generalization than the remarkable zero-shot methods. In this section, we empirically validate the effectiveness of our CATEX on real-word large-scale classification and OOD detection tasks. |
| Researcher Affiliation | Collaboration | 1Zhejiang University, 2Alibaba Cloud |
| Pseudocode | No | The paper describes its methods in prose and figures, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a direct link to its own source code or explicitly state that its implementation code is publicly available. |
| Open Datasets | Yes | Datasets. Following the common benchmarks in the literature [59, 50, 60, 38], we mainly consider the large-scale Image Net [11] as the in-distribution data. Subsets of i Naturalist [53], SUN [65], Places [69], and Texture [8] are adopted as the OOD datasets. |
| Dataset Splits | Yes | For pre-processing, we follow Ridnik et al [42] to clean invalid classes, allocating 50 images per class for validation, and crop-resizing all the images to 224 resolution. |
| Hardware Specification | Yes | We use Python 3.7.13 and Py Torch 1.8.1, and 2 NVIDIA V100-32G GPUs. |
| Software Dependencies | Yes | We use Python 3.7.13 and Py Torch 1.8.1, and 2 NVIDIA V100-32G GPUs. |
| Experiment Setup | Yes | Following the default setting [71], each context consists of 16 learnable 512-D prompt embeddings, which are trained for 50 epochs using the SGD optimizer with a momentum of 0.9. The initial learning rate is 0.002, which is decayed by the cosine annealing rule. |