Dual Expert Distillation Network for Generalized Zero-Shot Learning
Authors: Zhijie Rao, Jingcai Guo, Xiaocheng Lu, Jingming Liang, Jie Zhang, Haozhao Wang, Kang Wei, Xiaofeng Cao
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various benchmark datasets indicate a new state-of-the-art. |
| Researcher Affiliation | Academia | 1Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR, China 2Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, China 3Department of Computer Science and Engineering, HKUST, Hong Kong SAR, China 4Department of Computer Science, The University of Iowa, Iowa City, United States 5School of Computer Science and Technology, HUST, Wuhan, China 6School of Artificial Intelligence, Jilin University, Changchun, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at github.com/zjrao/DEDN. |
| Open Datasets | Yes | We conduct extensive experiments on three benchmark datasets to verify the effectiveness of the method, including CUB (Caltech UCSD Birds 200) [Wah et al., 2011], SUN (SUN Attribute) [Patterson and Hays, 2012], and AWA2 (Animals with Attributes 2) [Xian et al., 2017]. |
| Dataset Splits | Yes | We split all datasets following [Xian et al., 2017]. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Res Net-101' as a fixed feature extractor and 'GloVe' for semantic vectors, but does not provide specific version numbers for these or any other software dependencies, such as programming languages or ML frameworks. |
| Experiment Setup | Yes | We set the batch size to 50 and the learning rate to 0.0001. The RMSProp optimizer with the momentum set as 0.9 and weight decay set as 1e-4 is employed. For hyperparameters, [β, γ] are fixed to [0.001, 0.1]. We empirically set [λrc, λe] to [0.8, 0.9] for CUB, [0.95, 0.3] for SUN, [0.8, 0.5] for AWA2. |