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..
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 | Venue PDF | 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. |