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 | Conference PDF | Archive PDF | Plain Text | 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). |