How Does Unlabeled Data Provably Help Out-of-Distribution Detection?
Authors: Xuefeng Du, Zhen Fang, Ilias Diakonikolas, Yixuan Li
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | 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. |