Learning to Shape In-distribution Feature Space for Out-of-distribution Detection
Authors: Yonggang Zhang, Jie Lu, Bo Peng, Zhen Fang, Yiu-ming Cheung
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluations across mainstream OOD detection benchmarks empirically manifest the superiority of the proposed DRL over its advanced counterparts. |
| Researcher Affiliation | Academia | 1Hong Kong Baptist University 2Australian Artificial Intelligence Institute, University of Technology Sydney |
| Pseudocode | No | The paper describes its algorithm steps in prose within the 'DRL as Expectation-Maximization' section but does not include a formally labeled 'Algorithm' or 'Pseudocode' block. |
| Open Source Code | No | We will release our code upon acceptance. |
| Open Datasets | Yes | Following the setup in [46, 40], we consider CIFAR-10 [30] and CIFAR-100 [30] as ID datasets and train Res Net-18 [20] and Res Net-34 [20] on them respectively. |
| Dataset Splits | No | The paper describes training and testing procedures but does not explicitly mention or detail a specific 'validation' data split for hyperparameter tuning or early stopping. |
| Hardware Specification | Yes | We perform all experiments on an NVIDIA A100 GPU using Pytorch. |
| Software Dependencies | No | The paper mentions 'Pytorch' but does not specify a version number or list other software dependencies with their versions. |
| Experiment Setup | Yes | We train the model using stochastic gradient descent with momentum 0.9, and weight decay 10 4 for 500 epochs. The initial learning rate is 0.5 with cosine scheduling and the batch size is 512. |