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..
Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability
Authors: Jianing Zhu, Hengzhuang Li, Jiangchao Yao, Tongliang Liu, Jianliang Xu, Bo Han
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments and analysis demonstrate the effectiveness of our method. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Hong Kong Baptist University 2CMIC, Shanghai Jiao Tong University 3Shanghai AI Laboratory 4Mohamed bin Zayed University of Artificial Intelligence 5Sydney AI Centre, The University of Sydney. |
| Pseudocode | Yes | We present the algorithms of UM (in Algorithm 1) and UMAP (in Algorithm 2) in Appendix F. |
| Open Source Code | Yes | The code is available at: https://github.com/ tmlr-group/Unleashing-Mask. |
| Open Datasets | Yes | CIFAR-10, CIFAR-100 (Krizhevsky, 2009) as our major ID datasets, and we also adopt Image Net (Deng et al., 2009) for performance exploration. |
| Dataset Splits | Yes | To choose the parameters of the estimated loss constraint, we use the Tiny Image Net (Tavanaei, 2020) dataset as the validation set |
| Hardware Specification | Yes | All experiments are conducted with multiple runs on NVIDIA Tesla V100-SXM2-32GB GPUs with Python 3.6 and Py Torch 1.8. |
| Software Dependencies | Yes | All experiments are conducted with multiple runs on NVIDIA Tesla V100-SXM2-32GB GPUs with Python 3.6 and Py Torch 1.8. |
| Experiment Setup | Yes | We conduct all major experiments on Dense Net-101 (Huang et al., 2017) with training epochs fixed to 100. The models are trained using stochastic gradient descent (Kiefer & Wolfowitz, 1952) with Nesterov momentum (Duchi et al., 2011). We adopt Cosine Annealing (Loshchilov & Hutter, 2017) to schedule the learning rate which begins at 0.1. We set the momentum and weight decay to be 0.9 and 10 4 respectively throughout all experiments. The size of the mini-batch is 256 for both ID samples (during training and testing) and OOD samples (during testing). |