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..
How Does Unlabeled Data Provably Help Out-of-Distribution Detection?
Authors: Xuefeng Du, Zhen Fang, Ilias Diakonikolas, Yixuan Li
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, SAL achieves state-of-the-art performance on common benchmarks, reinforcing our theoretical insights. |
| Researcher Affiliation | Academia | 1Department of Computer Sciences, University of Wisconsin-Madison 2Australian Artificial Intelligence Institute, University of Technology Sydney |
| Pseudocode | Yes | A pseudo algorithm of SAL is in Appendix (see Algorithm 1). |
| Open Source Code | Yes | Code is publicly available at https://github.com/deeplearning-wisc/sal. |
| Open Datasets | Yes | WOODS considered CIFAR-10 and CIFAR-100 (Krizhevsky et al., 2009) as ID datasets (Pin). |
| Dataset Splits | No | The paper mentions splitting CIFAR datasets into two halves for ID training and wild mixture creation but does not explicitly specify a validation dataset split. |
| Hardware Specification | Yes | We run all experiments with Python 3.8.5 and Py Torch 1.13.1, using NVIDIA Ge Force RTX 2080Ti GPUs. |
| Software Dependencies | Yes | We run all experiments with Python 3.8.5 and Py Torch 1.13.1, using NVIDIA Ge Force RTX 2080Ti GPUs. |
| Experiment Setup | Yes | We train the ID classifier hw using stochastic gradient descent with a momentum of 0.9, weight decay of 0.0005, and an initial learning rate of 0.1. We train for 100 epochs using cosine learning rate decay, a batch size of 128, and a dropout rate of 0.3. |