Towards Optimal Feature-Shaping Methods for Out-of-Distribution Detection
Authors: Qinyu Zhao, Ming Xu, Kartik Gupta, Akshay Asthana, Liang Zheng, Stephen Gould
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments, we show that the feature-shaping function optimized by our method improves the generalization ability of OOD detection across a large variety of datasets and model architectures. |
| Researcher Affiliation | Collaboration | Qinyu Zhao1, Ming Xu1, Kartik Gupta2, Akshay Asthana2, Liang Zheng1, Stephen Gould1 1 The Australian National University 2 Seeing Machines Ltd |
| Pseudocode | No | The paper describes methods verbally and with equations, but does not include structured pseudocode or algorithm blocks with labels like 'Algorithm' or 'Pseudocode'. |
| Open Source Code | Yes | 1Our code is available at https://github.com/Qinyu-Allen-Zhao/OptFSOOD. |
| Open Datasets | Yes | We use CIFAR10, CIFAR100 (Krizhevsky et al., 2009), and Image Net-1k (Russakovsky et al., 2015) as ID datasets. |
| Dataset Splits | Yes | We extract ISFI vectors for the Image Net-1k training set (ID) (Russakovsky et al., 2015) and for the i Naturalist dataset (OOD) (Van Horn et al., 2018), using a Res Net50 model pre-trained on Image Net-1k training set. Specifically, we utilize Image Net-1k validation set (ID) and i Naturalist (OOD) with Res Net50. |
| Hardware Specification | Yes | All experiments are run on a single NVIDIA Ge Force RTX 4090 GPU. |
| Software Dependencies | No | The paper mentions software like 'Py Torch' and specific models but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Hyperparameters are set consistent with the original papers and concrete hyperparameter settings can be found in Section A.1 of the Appendix. |