Exploring Diverse Representations for Open Set Recognition
Authors: Yu Wang, Junxian Mu, Pengfei Zhu, Qinghua Hu
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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 wang.yu@tju.edu.cn, jxmu@tju.edu.cn, zhupengfei@tju.edu.cn, huqinghua@tju.edu.cn |
| 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. |