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..
Exploring Diverse Representations for Open Set Recognition
Authors: Yu Wang, Junxian Mu, Pengfei Zhu, Qinghua Hu
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on standard and OSR largescale benchmarks. Results show that the proposed discriminative method can outperform existing generative models by up to 9.5% on AUROC and achieve new state-of-the-art performance with little computational cost. |
| Researcher Affiliation | Academia | Yu Wang, Junxian Mu, Pengfei Zhu*, Qinghua Hu 1College of Intelligence and Computing, Tianjin University EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/Vanixxz/MEDAF. |
| Open Datasets | Yes | we conducted the experiments on five image datasets, including CIFAR10 (Krizhevsky 2009), CIFAR+10, CIFAR+50, SVHN (Netzer et al. 2011), and Tiny-Image Net (Pouransari and Ghili 2014). |
| Dataset Splits | Yes | To avoid unfair comparisons arising from different splits, we adopted unified split information with (Moon et al. 2022; Neal et al. 2018) and can be found in the supplementary. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python version, library versions like PyTorch or TensorFlow) beyond mentioning the use of Res Net18 and SGD optimizer. |
| Experiment Setup | Yes | In terms of optimization, we used an SGD optimizer with a momentum value of 0.9 and set the initial learning rate to 0.1 with a fixed batch size of 128 for 150 epochs. |