DOS: Diverse Outlier Sampling for Out-of-Distribution Detection
Authors: Wenyu Jiang, Hao Cheng, MingCai Chen, Chongjun Wang, Hongxin Wei
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the superiority of DOS, reducing the average FPR95 by up to 25.79% on CIFAR-100 with TI-300K. |
| Researcher Affiliation | Academia | 1Department of Statistics and Data Science, Southern University of Science and Technology 2State Key Laboratory for Novel Software Technology, Nanjing University |
| Pseudocode | Yes | Algorithm 1: DOS: Diverse Outlier Sampling |
| Open Source Code | Yes | Code is available at: https://github.com/lygjwy/DOS. |
| Open Datasets | Yes | Datasets. We conduct experiments on CIFAR100 (Krizhevsky & Hinton, 2009) as common benchmark and Image Net-10 (Ming et al., 2022a) as large-scale benchmark. For CIFAR100, a down-sampled version of Image Net (Image Net-RC) (Chen et al., 2021) is utilized as an auxiliary OOD training dataset. Additionally, we use the 300K random Tiny Images subset (TI-300K)1 as an alternative OOD training dataset, due to the unavailability of the original 80 Million Tiny Images2 in previous work (Hendrycks et al., 2019b). 1https://github.com/hendrycks/outlier-exposure |
| Dataset Splits | No | The paper mentions training and test datasets but does not explicitly provide details about a separate validation dataset split (e.g., specific percentages or sample counts) used for hyperparameter tuning or early stopping. While FPR95 at 95% TPR on ID data implies a threshold selection, it does not constitute a distinct validation *split*. |
| Hardware Specification | Yes | All the experiments are conducted on NVIDIA V100 and all methods are implemented with default parameters using Py Torch. |
| Software Dependencies | No | The paper states that methods are 'implemented with default parameters using Py Torch,' but it does not provide a specific version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | The model is trained for 100 epochs using SGD with a momentum of 0.9, a weight decay of 0.0001, and a batch size of 64, for both ID and OOD training data. The initial learning rate is set as 0.1 and decays by a factor of 10 at 75 and 90 epochs. Without tuning, we keep the number of the clustering center the same as the batch size. The model is fine-tuned for 10 epochs using SGD with a momentum of 0.9, a learning rate of 0.001, and a weight decay of 0.00001. |