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..
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 | Venue PDF | 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. |